Text summarization involves generating a summary of a document using computer programs. It is needed because the amount of textual information is growing rapidly, making it difficult for users to read everything. There are two main types of summarization: extraction, which selects important sentences from the original text, and abstraction, which generates a summary using semantic analysis. The document describes and compares two summarization algorithms - reduction and intersection - and provides screenshots of a program implementing the algorithms. It concludes that reduction creates better summaries but is slower, while intersection works well on some documents but often generates very short summaries.

1. Text Summarization
For Review And Feedback
BY :Aman Sadhwani
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2. What is Text Summarization?
And why we need it?
• We can define summary as a text which reflects the main and important sentences from
the original text. In Text summarization, Summary is generated by Computer.
• In Recent Years we are witnessing the amount of textual information is increasing day by
day .The Textual Information grows rapidly. It becomes more difficult for the user to read
the textual information and also it leads to loss of interest. That is the reason why Text
Summarization came into picture which will solve this problem.
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3. Types of Text Summarization
 1) Extraction: - In Extractive text summarization , summary is generated by selecting a set
of words, phrases, paragraph or sentences from the original document.
 2) Abstraction: - Abstractive methods are based on semantic representation and then use
natural language processing techniques to generate a summary that is nearer to
summary generated manually. This kind of summary may contain words that are not found
in the original document. Currently research is going on this method and demand for this
method is more.
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4. Proposed System
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 We have developed and compared two text summarization techniques
1) Reduction based
2) Inter section based

5. How Reduction Algorithm Works
 Step 1 - It takes a text as input.
 Step 2 - Splits it into one or more paragraph(s).
 Step 3 - Splits each paragraph into one or more sentence(s).
 Step 4 - Splits each sentence into one or more words.
 Step 5 - Gives each sentence weight-age (a floating point value) by comparing Its words
to a pre-defined dictionary called "stopWords.txt“
 If some word of a sentence matches to any word with the pre-defined Dictionary, then
the word is considered as Low weighted.
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6. Cont..
 Step 6 - An ordered list of weighted sentences is then prepared (Relatively High weighted
sentences comes first and low weighted sentences comes At last position).
 Step 7 - Now, we have the ordered list of weighted sentences, it continues to Store each
sentence (from ordered weighted sentences) in the output Variable (i.e. a list) until it
reaches the reduction ratio (It uses A formula to determine max number of sentences to
put in the output List)
 Step 8 - The output list is then returned.
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7. How InterSection Algorithm Works?
1. Split input text into Paragraph.
2. Split paragraph into sentences.
3. Split sentences into words.
4. Calculate the intersection between 2 sentences.
5. Remove non-alphabetic characters from sentence.
6. Convert content into dictionary.
7. Build the sentence dictionary.
8. Return best sentences in a paragraph.
9. Get the best sentences according to dictionary.
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8. Flow Chart
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9. Screen shots
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15. Conclusion
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16. Cont…
 By looking at last table we can say that intersection is faster than reduction
 But reduction creates better summary than intersection.
 Intersection works fine on some documents but generates only 1 or 2 line of summary on
some documents.
 This is because intersection is the most basic algorithm for text summarization. It doesn’t
use any NLP libraries like reduction.
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17. Hardware & Software requirement
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 Minimum Hardware Requirements
 Processor : Intel Pentium II or Higher
 RAM : 128 Mb or Higher
 Monitor ,Keyboard, Mouse
 Printer (Optional)
 Hard disk : 20 GB Or Higher
 Software Requirements
 OS: Windows xp or higher
 Java Installed On Machine
 Python 2.7 installed on machine.

18. Tools used
 NetBeans
 Python 2.7 IDLE
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19. References
 http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume22/erkan04a-html/erkan04a.html
 http://www.iajet.org/iajet_files/vol.1/no.4/Text%20Summarization%20Extraction%20System%
20TSES%20Using%20Extracted%20Keywords_doc.pdf
 http://en.wikipedia.org/wiki/Sentiment_analysis
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20. Future enhancement
 Will support summarization for multiple file types.
 User wise Document management.
 Multi document summarization.
 Improved summarization algorithms.
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21. THANK YOU
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