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Information  Highlighting <ul><li>Tim Ostler Cognitive Architecture Anaphora Ltd  [email_address] </li></ul><ul><li>InfoVi...
Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation  </li></ul><ul><li>3 Past stu...
1 Highlighters 1 Origins 2  Cognitive function 3 Highlighting for others
Highlighters 1/3 Origins <ul><li>1960s: use of  yellow fibre or felt pens  to highlight text begins in the USA </li></ul><...
Highlighters 2/3 Cognitive function <ul><li>Highlighting  feels  as though it helps revising, perhaps by encoding or  prim...
<ul><li>Also used to mark up a text for  selective attention  of another person </li></ul><ul><li>This  function chosen fo...
Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation  </li></ul><ul><li>3 Past stu...
2 Highlighting as information visualisation 1 Syntax highlighting 2  SeeSoft 3 TextLight 4 Readers vs. Authors
Highlighting as info visualisation 1/4 Syntax highlighting <ul><li>Highlighting can be seen as a means of visualising the ...
Highlighting as info visualisation 2/4 SeeSoft <ul><li>One of a suite of  text structure visualisation  tools from team le...
Highlighting as info visualisation 3/4 TextLight <ul><li>TextLight </li></ul><ul><ul><li>Conceived as a tool to  </li></ul...
Highlighting as info visualisation 4/4 Readers vs. authors <ul><li>For  readers ,   no benefits  from using  different col...
Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation  </li></ul><ul><li>3 Past stu...
3 Past studies on visual cueing 1 Judging importance 2  Choosing words 1 3 Choosing words 2 4 Core content 5 How many word...
<ul><li>Herbert Dreyfus: is the ability to tell the important from the unimportant a  fundamentally human  cognitive opera...
<ul><li>Weakness of all research: no formal rules on  which text  to cue </li></ul><ul><ul><li>Foster (1979): 26 students ...
<ul><li>Other experiments  </li></ul><ul><ul><li>Klare et al (1955) cued  single words </li></ul></ul><ul><ul><li>Dearborn...
Past studies 4/6 “Core” content <ul><li>Most  specific  suggestions by Hershberger & Terry (1965) </li></ul><ul><ul><li>“ ...
<ul><li>Crouse & Ildstein (1972) </li></ul><ul><ul><li>Density  of cued material influences its effect </li></ul></ul><ul>...
<ul><li>Fowler & Barker (1974) </li></ul><ul><ul><li>Pointed to the  large variance  (4% to 32%) observed in the proportio...
Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation  </li></ul><ul><li>3 Past stu...
4 User study 1 Experimental procedure 2  Analytical procedure  3 Analysis of results 4 Observations
<ul><li>11 subjects provided with an 1111-word article from the financial times IT supplement, with instructions to imagin...
<ul><li>Article  input into spreadsheet as  left axis  of spreadsheet spanning 1111 rows (one word per row) </li></ul><ul>...
<ul><li>Results show  wide variance  in  number  of words highlighted </li></ul><ul><ul><li>Minimum of 50 (4.5%) </li></ul...
<ul><li>None of subjects made highlighting decisions before having read  at least one paragraph   </li></ul><ul><li>Large ...
Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation  </li></ul><ul><li>3 Past stu...
5 Heuristics 1 Correlation with average choice 2  Key correlations 3 Best heuristics 4 Highlighting by humans 1 5 Highligh...
<ul><li>Average correlation between any  one person’s   highlighting decisions  and the scores for   probability of given ...
Heuristics 2/7 Key correlations
<ul><li>Most successful heuristics: </li></ul><ul><ul><li>1 Word should be part of  first statement in a discourse segment...
Heuristics 4/7 Highlighting by humans 1 <ul><li>Areas where probability of highlighting is greater than  0.4 </li></ul>
Heuristics 5/7 Highlighting by humans 2 <ul><li>Areas where probability of highlighting is greater than  0.33 </li></ul>
Heuristics 6/7 Highlighting by best heuristics <ul><li>KEY </li></ul><ul><li>First statement in a quote </li></ul><ul><li>...
<ul><li>Best combination of heuristics produced correlation with actual highlighting probability of  0.56   (average of 0....
Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past stud...
6 Identifying discourse markers 1 Segments 2  Statements 3 Solution stages 4 Stage labels 5 Cue words as signals 6 “Soluti...
Identifying discourse markers 1/6 Segments <ul><li>Different means of discourse segmentation beyond the scope of this pape...
Identifying discourse markers 2/6 Statements <ul><li>Sometimes preceded or followed by a  coherence relation  — a question...
Identifying discourse markers 3/6 Solution stage <ul><li>“ Situation-problem-solution-evaluation ”  structure </li></ul><u...
Identifying discourse markers 4/6 Stage signals <ul><li>Hoey (1994) — elements of structure often signalled by  characteri...
Identifying discourse markers 5/6 Cue words as signals <ul><li>Hoey (ibid.): Discourse structure essentially  evaluative  ...
Identifying discourse markers 6/6   “Solution” signals <ul><li>TextLight need only be concerned with  “solution” signals <...
Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past stud...
7 “Given” and “new” information 1 Highlighting the new 2  Narrative stages 3 Importance 4 Intonation 5 First statement 6 L...
“ Given” and “new” information 1/9 Highlighting the new <ul><li>Why  were best heuristics more effective than others?  </l...
&quot;Given&quot; and ”new&quot; information 2/9 Narrative stages <ul><li>Theory supported by the fact that  80%  of subje...
“ Given” and “new” information 3/9 Importance <ul><li>We can argue that an idea’s  perceived importance  is judged accordi...
“ Given” and “new” information 4/9  Intonation <ul><li>Halliday (1970) — in spoken discourse,  intonation  is used to sign...
“ Given” and “new” information 5/9  First statement <ul><li>First statement in a paragraph can be considered as  supportin...
“ Given” and “new” information 6/9  Lists <ul><li>Lists typically act as  systematic tabulation  of what the author believ...
“ Given” and “new” information 7/9 “Solution” as “new ” <ul><li>Solution stages comprise “new” information: a  climactic p...
“ Given” and “new” information 8/9 Quasi-revision <ul><li>Criteria and procedure would have been different for quasi-revis...
“ Given” and “new” information 9/9 Levels of “newness” <ul><li>Information can also be perceived as “new” at  several leve...
Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past stud...
6  Future directions 1 Highlighting long neglected 2  Virtues of highlighting 3 TextLight: to do
Future directions 1/3 Highlighting long neglected <ul><li>The study of the  selection of words for highlighting  previousl...
Future directions 2/3  Virtues of highlighters <ul><li>Output  familiar to users </li></ul><ul><li>Highlighting shown to b...
Future directions 3/3  TextLight: to do <ul><li>Incorporate discourse  segmentation algorithms </li></ul><ul><li>Complete ...
TextLight   URLs <ul><li>http://www.cogarch.demon.co.uk/textlight.html </li></ul><ul><li>mailto:timo@cogarch.com </li></ul>
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Information Highlighting

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A study of the discourse-analytical and other textual criteria people use to select words when they are highlighting a text for others.

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Information Highlighting

  1. 1. Information Highlighting <ul><li>Tim Ostler Cognitive Architecture Anaphora Ltd [email_address] </li></ul><ul><li>InfoVis’99 London 16 July 1999 </li></ul>
  2. 2. Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past studies of visual cueing </li></ul><ul><li>4 User study </li></ul><ul><li>5 Heuristics </li></ul><ul><li>6 Identifying discourse markers </li></ul><ul><li>7 “Given” and “new” information </li></ul><ul><li>8 Future directions </li></ul>
  3. 3. 1 Highlighters 1 Origins 2 Cognitive function 3 Highlighting for others
  4. 4. Highlighters 1/3 Origins <ul><li>1960s: use of yellow fibre or felt pens to highlight text begins in the USA </li></ul><ul><li>1971: Schwan-Stabilo of West Germany launches first fluorescent highlighter pen </li></ul>
  5. 5. Highlighters 2/3 Cognitive function <ul><li>Highlighting feels as though it helps revising, perhaps by encoding or priming material for incorporation into long-term memory </li></ul><ul><li>Partly confirmed by research: Hult et al. (1984) found that note-taking does involve semantic encoding </li></ul>
  6. 6. <ul><li>Also used to mark up a text for selective attention of another person </li></ul><ul><li>This function chosen for study, because of clear application to information overload </li></ul><ul><li>Conducted user study to define suitable heuristics for text selection </li></ul>Highlighters 3/3 Highlighting for others
  7. 7. Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past studies of visual cueing </li></ul><ul><li>4 User study </li></ul><ul><li>5 Heuristics </li></ul><ul><li>6 Identifying discourse markers </li></ul><ul><li>7 “Given” and “new” information </li></ul><ul><li>8 Future directions </li></ul>
  8. 8. 2 Highlighting as information visualisation 1 Syntax highlighting 2 SeeSoft 3 TextLight 4 Readers vs. Authors
  9. 9. Highlighting as info visualisation 1/4 Syntax highlighting <ul><li>Highlighting can be seen as a means of visualising the logical or conceptual structure of a text </li></ul><ul><ul><li>Enhances understanding of text </li></ul></ul><ul><ul><li>Guides eye to most important passages </li></ul></ul><ul><li>Principle is widely demonstrated by the syntax highlighting in text-editors for programmers </li></ul><ul><ul><li>Useful : need to visualize logical structure acute </li></ul></ul><ul><ul><li>Easy : programming languages offer finite and precise set of cues for editors to detect and colour </li></ul></ul>
  10. 10. Highlighting as info visualisation 2/4 SeeSoft <ul><li>One of a suite of text structure visualisation tools from team led by Stephen Eick at Lucent (formerly Bell) Laboratories </li></ul><ul><li>Each line of code reduced to a line of single pixel thickness , coloured according to a range of user-specified criteria </li></ul><ul><li>Thousands of lines of code can be displayed on the screen at once </li></ul>
  11. 11. Highlighting as info visualisation 3/4 TextLight <ul><li>TextLight </li></ul><ul><ul><li>Conceived as a tool to </li></ul></ul><ul><ul><ul><li>Detect certain attributes of a text’s cognitive structure </li></ul></ul></ul><ul><ul><ul><li>Encode them in visual, non-lexical form </li></ul></ul></ul><ul><ul><ul><li>Superimpose them in place on the corresponding text </li></ul></ul></ul><ul><ul><li>Like a GIS, can reveal attributes of its data set that would otherwise be obscured, throwing the underlying structure into high relief </li></ul></ul>
  12. 12. Highlighting as info visualisation 4/4 Readers vs. authors <ul><li>For readers , no benefits from using different colours for different categories of &quot;new&quot; information </li></ul><ul><li>But for authors and text analysts extending TextLight to identify text attributes is as valuable as colouring different CAD layers to architects </li></ul><ul><li>Revealing the pattern of distribution of attributes such as readability or levels of completion like a knowledge discovery system for authors </li></ul>
  13. 13. Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past studies of visual cueing </li></ul><ul><li>4 User study </li></ul><ul><li>5 Heuristics </li></ul><ul><li>6 Identifying discourse markers </li></ul><ul><li>7 “Given” and “new” information </li></ul><ul><li>8 Future directions </li></ul>
  14. 14. 3 Past studies on visual cueing 1 Judging importance 2 Choosing words 1 3 Choosing words 2 4 Core content 5 How many words? 6 Large variance
  15. 15. <ul><li>Herbert Dreyfus: is the ability to tell the important from the unimportant a fundamentally human cognitive operation? </li></ul><ul><li>Perhaps, but in some genres widespread agreement on signals for different stages in a discourse </li></ul><ul><li>So while we can’t tell what seems important for every person, we can assess what is being presented as important </li></ul>Past studies 1/6 Judging importance
  16. 16. <ul><li>Weakness of all research: no formal rules on which text to cue </li></ul><ul><ul><li>Foster (1979): 26 students and lecturers given 3400-word text and asked to underline sentences containing key ideas author trying to put over </li></ul></ul><ul><ul><li>Half subjects told not to underline more than 16 sentences, half not more than 8 </li></ul></ul><ul><ul><li>First case: 213 selections spanned 80 sentences, with only 9 sentences selected by 6 or more </li></ul></ul><ul><ul><li>Second case: 102 selections distributed over 52 sentences, with only 2 selected by 6 or more </li></ul></ul><ul><li>Foster’s conclusion: difficult to identify sections for cueing </li></ul>Past studies 2/6 Choosing words 1
  17. 17. <ul><li>Other experiments </li></ul><ul><ul><li>Klare et al (1955) cued single words </li></ul></ul><ul><ul><li>Dearborn et al (1949) emphasised word carrying the &quot;peak stress&quot; in a sentence (did not describe how word selected) </li></ul></ul><ul><ul><li>Crouse & Ildstein (1972) cued statements or sentences </li></ul></ul>Past studies 3/6 Choosing words 2
  18. 18. Past studies 4/6 “Core” content <ul><li>Most specific suggestions by Hershberger & Terry (1965) </li></ul><ul><ul><li>“ Core” content made up 1/3 of total text length: </li></ul></ul><ul><ul><ul><li>New key words </li></ul></ul></ul><ul><ul><ul><li>Familiar key words </li></ul></ul></ul><ul><ul><ul><li>Key statements </li></ul></ul></ul><ul><ul><ul><li>Basic core statements </li></ul></ul></ul><ul><ul><ul><li>Key examples </li></ul></ul></ul><ul><ul><ul><li>Rephrasing of key statements </li></ul></ul></ul>
  19. 19. <ul><li>Crouse & Ildstein (1972) </li></ul><ul><ul><li>Density of cued material influences its effect </li></ul></ul><ul><li>Foster (1979) </li></ul><ul><ul><li>Optimal proportion of text to be highlighted still not established </li></ul></ul>Past studies 5/6 How many words?
  20. 20. <ul><li>Fowler & Barker (1974) </li></ul><ul><ul><li>Pointed to the large variance (4% to 32%) observed in the proportion of text highlighted by members of the test group who were asked to highlight for themselves </li></ul></ul><ul><li>Rickards & August (1975) </li></ul><ul><ul><li>Asked to highlight passages of structural importance, test subjects all chose passages that Rickards & August considered relatively unimportant </li></ul></ul>Past studies 6/6 Large variance
  21. 21. Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past studies of visual cueing </li></ul><ul><li>4 User study </li></ul><ul><li>5 Heuristics </li></ul><ul><li>6 Identifying discourse markers </li></ul><ul><li>7 “Given” and “new” information </li></ul><ul><li>8 Future directions </li></ul>
  22. 22. 4 User study 1 Experimental procedure 2 Analytical procedure 3 Analysis of results 4 Observations
  23. 23. <ul><li>11 subjects provided with an 1111-word article from the financial times IT supplement, with instructions to imagine they were corporate librarians identifying the key points in an article for a board member </li></ul><ul><li>Questionnaire sought: </li></ul><ul><ul><li>Subjects’ past experience of highlighting </li></ul></ul><ul><ul><li>Criteria for text selection </li></ul></ul><ul><ul><li>At what points made their selection </li></ul></ul><ul><ul><li>Other comments </li></ul></ul>User study 1/4 Experimental procedure
  24. 24. <ul><li>Article input into spreadsheet as left axis of spreadsheet spanning 1111 rows (one word per row) </li></ul><ul><li>Along the top of the spreadsheet entered the attributes for each word (36 categories) </li></ul><ul><li>For each word probability of lying in a highlighted passage given a decimal figure between 0 and 1 </li></ul><ul><li>All other parameters rebased to fall between 0 and 1 </li></ul><ul><li>Gave correlation of any given parameter with the probability that a word fell within a highlighted group of words </li></ul>User study 2/4 Analytical procedure
  25. 25. <ul><li>Results show wide variance in number of words highlighted </li></ul><ul><ul><li>Minimum of 50 (4.5%) </li></ul></ul><ul><ul><li>Maximum of 396 (35.64%) </li></ul></ul><ul><ul><li>(Fowler & Barker 1974: 4-32%) </li></ul></ul><ul><li>Marked difference between male and female subjects </li></ul><ul><ul><li>Males averaging 15% </li></ul></ul><ul><ul><li>Females 25.5% </li></ul></ul><ul><li>Little correlation between part of speech/syntactic role and probability of highlighting </li></ul><ul><li>Noticeable association with longer words </li></ul>User study 3/4 Analysis of results
  26. 26. <ul><li>None of subjects made highlighting decisions before having read at least one paragraph </li></ul><ul><li>Large majority (70%) delayed highlighting until whole passage read </li></ul><ul><li>Conclusion: decisions made at a discourse-analytical and not a strictly linguistic level </li></ul>User study 4/4 Observations
  27. 27. Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past studies of visual cueing </li></ul><ul><li>4 User study </li></ul><ul><li>5 Heuristics </li></ul><ul><li>6 Identifying discourse markers </li></ul><ul><li>7 “Given” and “new” information </li></ul><ul><li>8 Future directions </li></ul>
  28. 28. 5 Heuristics 1 Correlation with average choice 2 Key correlations 3 Best heuristics 4 Highlighting by humans 1 5 Highlighting by humans 2 6 Highlighting by best heuristics 7 Performance of best heuristics
  29. 29. <ul><li>Average correlation between any one person’s highlighting decisions and the scores for probability of given words being highlighted was 0.44 </li></ul><ul><li>For any individual word probability varied between 0 and 0.83 , offering clear guidelines for assessing any trial selection criteria </li></ul>Heuristics 1/7 Correlation with average choice
  30. 30. Heuristics 2/7 Key correlations
  31. 31. <ul><li>Most successful heuristics: </li></ul><ul><ul><li>1 Word should be part of first statement in a discourse segment </li></ul></ul><ul><ul><li>2 Word should be part of first statement in any quote not an immediate continuation of a previous quote </li></ul></ul><ul><ul><li>3 Word should be part of a list </li></ul></ul><ul><ul><li>4 Word should be part of “solution” stage </li></ul></ul>Heuristics 3/7 Best heuristics
  32. 32. Heuristics 4/7 Highlighting by humans 1 <ul><li>Areas where probability of highlighting is greater than 0.4 </li></ul>
  33. 33. Heuristics 5/7 Highlighting by humans 2 <ul><li>Areas where probability of highlighting is greater than 0.33 </li></ul>
  34. 34. Heuristics 6/7 Highlighting by best heuristics <ul><li>KEY </li></ul><ul><li>First statement in a quote </li></ul><ul><li>“ Solution” stage </li></ul><ul><li>First statement in a discourse segment </li></ul>
  35. 35. <ul><li>Best combination of heuristics produced correlation with actual highlighting probability of 0.56 (average of 0.43 for test subjects) </li></ul><ul><li>In other words, selecting text according to specified criteria achieved a correlation that was greater than all but one of the test subjects achieved and considerably higher than the average </li></ul><ul><li>BUT: challenge is to identify the markers denoting relevant features in a discourse </li></ul>Heuristics 7/7 Performance of best heuristics
  36. 36. Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past studies of visual cueing </li></ul><ul><li>4 User study </li></ul><ul><li>5 Heuristics </li></ul><ul><li>6 Identifying discourse markers </li></ul><ul><li>7 “Given” and “new” information </li></ul><ul><li>8 Future directions </li></ul>
  37. 37. 6 Identifying discourse markers 1 Segments 2 Statements 3 Solution stages 4 Stage labels 5 Cue words as signals 6 “Solution” signals
  38. 38. Identifying discourse markers 1/6 Segments <ul><li>Different means of discourse segmentation beyond the scope of this paper </li></ul><ul><li>Segments most often coincide with beginning of paragraphs, and normally begin with a proposition or assertion </li></ul><ul><li>Most effective technique found: select opening statement in its simplest form </li></ul>
  39. 39. Identifying discourse markers 2/6 Statements <ul><li>Sometimes preceded or followed by a coherence relation — a question or other linguistic feature that makes proposition’s relevance to the preceding text clear </li></ul><ul><li>Following text tends to fill out details and/or provide supporting evidence for the assertion </li></ul>
  40. 40. Identifying discourse markers 3/6 Solution stage <ul><li>“ Situation-problem-solution-evaluation ” structure </li></ul><ul><ul><li>Narrative structures </li></ul></ul><ul><ul><ul><li>Boy meets girl – boy loses girl – boy regains girl – boy & girl live happily ever after </li></ul></ul></ul><ul><ul><li>Feature articles </li></ul></ul><ul><ul><ul><li>Dogs make great pets – however they can get fleas – Winalot have now launched a new anti-flea dog food – owners have declared it a success) </li></ul></ul></ul>
  41. 41. Identifying discourse markers 4/6 Stage signals <ul><li>Hoey (1994) — elements of structure often signalled by characteristic words </li></ul><ul><li>Stage signals as the most basic level </li></ul><ul><ul><li>“ Cars are a common way of getting from A to B. However , the congestion that they cause is a problem. The solution is to get people to use public transport. In this way everyone can get to work quickly.” </li></ul></ul>
  42. 42. Identifying discourse markers 5/6 Cue words as signals <ul><li>Hoey (ibid.): Discourse structure essentially evaluative </li></ul><ul><ul><li>e.g. “If thyristors are used to control the motor of an electric car, the vehicle moves smoothly but with poor efficiency at low speeds” </li></ul></ul><ul><ul><li>“ Problem” stage signalled by negative evaluation “poor” </li></ul></ul><ul><li>So stages can be identified by spotting cue words or phrases </li></ul>
  43. 43. Identifying discourse markers 6/6 “Solution” signals <ul><li>TextLight need only be concerned with “solution” signals </li></ul><ul><li>Two examples of such signals </li></ul><ul><ul><li>Words to do with “ solving ”, “ developing ” or “ inventing ” </li></ul></ul><ul><ul><li>Change of verb form into the present perfect tense , as in &quot;have -ed&quot;. Tense then reverts to simple present to denote that a new situation exists as a result of the solution </li></ul></ul>
  44. 44. Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past studies of visual cueing </li></ul><ul><li>4 User study </li></ul><ul><li>5 Heuristics </li></ul><ul><li>6 Identifying discourse markers </li></ul><ul><li>7 “Given” and “new” information </li></ul><ul><li>8 Future directions </li></ul>
  45. 45. 7 “Given” and “new” information 1 Highlighting the new 2 Narrative stages 3 Importance 4 Intonation 5 First statement 6 Lists 7 “Solution” as “new” 8 Quasi-revision 9 Levels of “new”
  46. 46. “ Given” and “new” information 1/9 Highlighting the new <ul><li>Why were best heuristics more effective than others? </li></ul><ul><li>Prague school (1930s) — information is composed of a mixture of “given” and “new” information </li></ul><ul><li>Proposition: essential factor behind the choice of text to highlight is that they are all ways in which “new” information is signalled at the discourse level </li></ul>
  47. 47. &quot;Given&quot; and ”new&quot; information 2/9 Narrative stages <ul><li>Theory supported by the fact that 80% of subjects stated that they were highlighting words that “ marked significant stages in the narrative .” </li></ul><ul><li>This implies information that is new in the context of preceding text </li></ul>
  48. 48. “ Given” and “new” information 3/9 Importance <ul><li>We can argue that an idea’s perceived importance is judged according to the extent to which it is: </li></ul><ul><ul><li>New as opposed to given </li></ul></ul><ul><ul><li>Matches a perceived gap in the structure of the reader’s domain knowledge </li></ul></ul><ul><li>When highlighting on behalf of others , we have to make informed judgement on how ultimate reader will define importance </li></ul>
  49. 49. “ Given” and “new” information 4/9 Intonation <ul><li>Halliday (1970) — in spoken discourse, intonation is used to signal to the listener what the speaker understands to be new information </li></ul><ul><li>Could highlighting perform equivalent function? </li></ul>
  50. 50. “ Given” and “new” information 5/9 First statement <ul><li>First statement in a paragraph can be considered as supporting structure for the statement at the beginning of the discourse segment that contains it </li></ul><ul><li>Operates as one of primary statements containing most of the “new” information in document </li></ul>
  51. 51. “ Given” and “new” information 6/9 Lists <ul><li>Lists typically act as systematic tabulation of what the author believes to be important (i.e. “new” and relevant) information </li></ul><ul><li>Often used for predictive purposes within a discourse, or for enumerating significant points </li></ul><ul><li>People therefore tend to identify lists as concentrated sources of meaning , and as such eligible for highlighting </li></ul><ul><li>Speaker might very well emphasise this by counting the points off using the fingers of his hand </li></ul>
  52. 52. “ Given” and “new” information 7/9 “Solution” as “new ” <ul><li>Solution stages comprise “new” information: a climactic point of novelty in schema, justifying status as “highlightable” text </li></ul><ul><li>If article modelled as histogram with columns depicting sentences plotted against new information content, highlighting like slicing across the graph using a threshold value </li></ul>
  53. 53. “ Given” and “new” information 8/9 Quasi-revision <ul><li>Criteria and procedure would have been different for quasi-revision </li></ul><ul><ul><li>Shorter range </li></ul></ul><ul><ul><li>More spontaneously applied </li></ul></ul><ul><li>Reader has more detailed knowledge of what is “new” info for him/herself </li></ul><ul><li>Highlighting can be done </li></ul><ul><ul><li>In real time </li></ul></ul><ul><ul><li>With greater precision </li></ul></ul>
  54. 54. “ Given” and “new” information 9/9 Levels of “newness” <ul><li>Information can also be perceived as “new” at several levels: </li></ul><ul><ul><li>Within a sentence , particular words can be seen as new </li></ul></ul><ul><ul><li>Within a paragraph , some sentences can be interpreted as new and others as contextual or supporting information </li></ul></ul><ul><ul><li>Within a discourse segment or discourse , still longer passages may be perceived as containing “new” information </li></ul></ul>
  55. 55. Summary <ul><li>1 Highlighters </li></ul><ul><li>2 Highlighting as information visualisation </li></ul><ul><li>3 Past studies of visual cueing </li></ul><ul><li>4 User study </li></ul><ul><li>5 Heuristics </li></ul><ul><li>6 Identifying discourse markers </li></ul><ul><li>7 “Given” and “new” information </li></ul><ul><li>8 Future directions </li></ul>
  56. 56. 6 Future directions 1 Highlighting long neglected 2 Virtues of highlighting 3 TextLight: to do
  57. 57. Future directions 1/3 Highlighting long neglected <ul><li>The study of the selection of words for highlighting previously neglected </li></ul><ul><li>Potential of automatic highlighting as a tool to handle information overload also neglected </li></ul>
  58. 58. Future directions 2/3 Virtues of highlighters <ul><li>Output familiar to users </li></ul><ul><li>Highlighting shown to be helpful in content recall </li></ul><ul><li>Addresses issue of confidence </li></ul><ul><ul><li>Highlighting acts not as a censor but as a guide : non-selected text (and therefore the context) always in view </li></ul></ul><ul><li>Suitable as a plug-in module for other programs </li></ul>
  59. 59. Future directions 3/3 TextLight: to do <ul><li>Incorporate discourse segmentation algorithms </li></ul><ul><li>Complete lexical dictionary for cue recognition </li></ul><ul><li>Port from Prolog to Java for greater portability </li></ul>
  60. 60. TextLight URLs <ul><li>http://www.cogarch.demon.co.uk/textlight.html </li></ul><ul><li>mailto:timo@cogarch.com </li></ul>

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