This document presents a Hindi text summarization system. It discusses how text summarization extracts important sentences from documents to provide short, vital summaries. The proposed system preprocesses Hindi text, extracts sentence features, ranks sentences, and outputs a summary. Features considered include word frequency, sentence length, position, and title words. Sentence ranking is calculated based on normalized feature values to select important sentences for the summary. Future work may include additional features and algorithms to optimize the summarization model.

1. HINDI TEXT SUMMERIZATION
o Abhishek Kumar (CSE - 10114008
o Nishant Kumar (CSE – 10114017)
Presented by:Guided by:
• Dr. Manjira Sinha

2.  Introduction
Text Summarization is an algorithm that extracts sentences from
a text document, determines which are most important, and
returns with short (usually half the size), vital information in a
readable and structured way.
 It provides the reader with filtered description of source text and a
non redundant presentation of facts found in the text.

3.  Why we need Text-Summarization
 Business leaders, analysts, students and academic researchers
need to go through huge numbers of documents every day to
keep ahead, and a large portion of their time is spent just
figuring out what document is relevant and what isn’t.+
 By extracting important sentences and creating comprehensive
summaries, it’s possible to quickly assess whether or not a
document is worth reading.
• Headlines of news
• Abstract summary of technical paper
• Review of book or Preview of a Movie

4.  Why choose Hindi as language of study ?
 Native language of most of the people in Bihar,
Jharkhand, UP, MP Delhi, Chhattisgarh, Himachal Pradesh,
Haryana, Rajsthan etc.
 Lots of work done on English language relatively very few
have shown interest in Hindi language.
 Official Language of India.
It is written in the Devanagari script which has largest
alphabet set.

5.  Approaches Of Summarization
Extraction - Based
 Statistical
 Linguistic
 Hybrid
Abstraction - Based

6.  Proposed System
1. •Hindi Text Document
2. •Preprocessing
3. •Extracting sentence features
4. •Sentence Ranking
5. •Summary

7.  Preprocessing
 Sentence Segmentation
 पूर्ण विराम (|)
Tokenization
 पूर्ण विराम (|), उपविराम (:), अर्ण विराम (;)
Stopwords Removal
 कारक (ने, को, से, के लिए, में, पर)
 सिणनाम (आप, तू, यह, िह, कु छ )
 समुच्चयबोर्क अव्यय(और, िेककन, पर, एिं, इसलिए, मगर)
 समास (अनुसार, पयणन्त, िािा)
 Stemming
 Suffixes, plural are ignored (भारत,भारतीय)

8. Feature-Extraction
 Text Rank Feature
 Word frequency
(Sentence having most frequent word in paragraph will have high ranking.)
 Sentence Length
 Eliminate sentence too long or too short.
 Sentence Position
 Position of sentence in text, decides its importance.
 Beginning – Theme
 End – Conclude or summary
 Title Word Feature
 Sentences having words which matches with paragraph title words will be
included in summary.

9. Sentence Ranking & Summary
Calculation of Ranking value of each sentence based on our selected features.
Normalize the each feature ranking value in scale (0 to 1).
Add all feature ranking values and calculate final ranking for each sentence.
 Sort the final ranking of each sentence in descending order.
Based on percentage of summary requirement, select sentences in descending
order of ranking values.
Print the summary of paragraph in order of original paragraph.

10. Future Work
 We Will add more features like :
 Proper Noun, Numerical data, Sentence similarity.
 To optimize our algorithm
 Genetic Algorithm (GA)
 Artificial Neural Network (ANN)
 We will make a GUI based Software for better user
experience.

11. References
 Yihong Gong, Xin Liu : Generic Text Summarization Using Relevance Measure and
Latent Semantic Analysis
 Gunes Erkan, Dragomir R. Radev : LexRank - Graph-based Lexical Centrality as
Salience in Text Summarization
 David Kirk Evans, Judith L. Klavans, Kathleen R. McKeown : Columbia Newsblaster:
Multilingual News Summarization on the Web
 Algorithmia : Introduction to Automatic Text Summarization
 Wikipedia : Automatic_summarization

12. Thank You for your Attention
र्न्यिाद !!