Comments Orinted Blog Summarizion project's Presentation Slides

1. Comments-Oriented Blog
Summarization

2. Motivation
 Comments left by readers on Web documents contain valuable
information that can be utilized in different information retrieval tasks
including document search, visualization, and summarization.
 In this project we aim to summarize a Web document (e.g. a blog post) by
considering the comments left by its readers.
 Web documents are now presented with annotations given by their
readers in the form of tags, comments, ratings, and others.
 These Annotations along with comments are valuable input from users and
can be utilized in different IR tasks.
 By considering these comments, the generated summary can better
capture the input from the readers, as opposed to the author of the
document only.
 Comments-oriented summary provides balanced views from both author
and readers.

3. Introduction
Problem Statement
Given a blog post, consisting of a set of sentences P = {s1 , s2 , . . . , sn} and
the set of comments C = {c1 , c2 , . . . c} associated with blog post , the task of
comments- oriented blog summarization is to extract a subset of sentences P ,
denoted by Sr (Sr ⊂ P ), that best represents the discussion in C
Solution
 Score blog sentences based on their similarity with top scored relevant
comments.
 Comments are scored by using RQT graph/tensor based approach and
Named Entity Similarity score.

4. Abstract Solution
Comment Oriented Blog Summarization

5. Approach
 In summary generation it is important to retrieve relevant comments.
 A comment is relevant if it reflects the topic discussed in blog or has more
replies.
 A comment is scored using RQT model and Named Entity Similarity model
and top comments are selected as relevant comments.
 Similarity score for each sentence is calculated by summation of cosine
similarity between that sentence and other comments.
 Top scored sentences are the ones which are grasped by most
commentators and hence are relevant for summary.

6. RQT Model
 Three factors determine RQT score (Rc) of a comment
 Response Count (Cr ) : Number of replies to each comment.
 Topic Related Cluster Count (Ct): Cosine similarity used to cluster comments
 Quotation Count(Cq ): Number of times it is quoted in other comments.
Rc= Cr+Ct +Cq

7. Additional Factors
 Likes Count (Cl) :Our dataset is Techcrunch.com where people comment
using facebook. Likes on a particular comment also increase relevance of
a comment significantly. Number of likes (Cl) also affect weightage.
Rc= Cr+Ct +Cq+Cl
 Named Entity Similarity: Named entites in a comment are identified by
Stanford POS tagger and named entity score (Ec) is calculated by taking
number of named entities in a comment.
Final Score for each comment is calculated as
Score(C) = Rc + Ec

8. Sentence Scoring
 Comments whose weights are greater than threshold value are chosen as
top comments.
 Cosine similarity of each sentence is calculated with top comments .
 Sentences are assigned score based on their cosine similarity with top
comments.
Score(Si)=Summation(CS(Si,Comments))
CS : Cosine Similarity Si : Blog Sentence
 Only those top 5~7 sentences which has more than 6~8 words will be
selected as summary for the blog. More or less number of sentences can
be selected based on percentage of summary required.

9. Experiments And Results
 10 blogs were randomly chosen with large number of comments and
generated a summary with 30 % and 20 % of words.
 Generated summaries using online tools and compared System generated
summaries using the ROUGE Summary Evaluation Package by Chin-Yew
LIN.

10. Conclusions
 Our approach depends upon number of factors like the number of likes
each comment has got, the length of the blog content etc
 Generated summary was awarded with less ROGUE score if the number of
comments aren't enough.
 Generated summary was less accurate when none of comments were
accurate
 By scoring the comments based on named entities, accuracy of ranking of
comments increased significantly.
 This system needs more testing and larger dataset in order to get optimal
values of the constants.