Automatic text summarization is the process of reducing the text content and retaining the important points of the document. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based text summarization can be divided into two phases: pre-processing and processing. In this paper, we discuss some of the extractive based text summarization approaches used by researchers. We also provide the features for extractive based text summarization process. We also present the available linguistic preprocessing tools with their features, which are used for automatic text summarization. The tools and parameters useful for evaluating the generated summary are also discussed in this paper. Moreover, we explain our proposed lexical chain analysis approach, with sample generated lexical chains, for extractive based automatic text summarization. We also provide the evaluation results of our system generated summary. The proposed lexical chain analysis approach can be used to solve different text mining problems like topic classification, sentiment analysis, and summarization.

1. Text Summarization Using NLP
Presented by,
Akash N. Karwande
(2016MNS011)
Guided by
Prof. R.K. Chavan

2. Introduction
The goal of summarization is to produce a shorter version of a source text by
preserving the meaning and the key contents of the original document.
A well written summary can significantly reduce the amount of work
needed to digest large amounts of text.

3. Types of Text summarization
There are two types summaries
1. Extractive summaries
2. Abstractive summaries

4. Extractive summaries
 Extractive summaries are created by reusing portions (words,
sentences, etc.) of the input text document
 The system extracts text from the entire collection, without modifying
the text document.
 Most of the summarization research today is on extractive
summarization.

5. Abstractive summaries
 Requires deep understanding and reasoning over the text
 It Provides own summary over input text without using same word or
sentence in the input text
 Determines the actual and short meaning of each element, such as
words,sentences and paragraphs

6. Natural Language Toolkit
 leading platform for building Python programs to work with human
language data
 NLP is a field of computer science, artificial intelligence (also called
machine learning), and linguistics processing
 Interactions between computers and human (natural) languages
 It provides suite of text processing libraries for classification, tokenization,
stemming, tagging, parsing, and semantic reasoning


7. Continued…
Following are the NLTK Libraries used in text summarization
 Word tokenizer
 Sentence tokenizer
 stopwords
 BeautifulSoup
 numpy library
 Tagging
 Parsing

8. Linguistic Preprocessing for Automatic Summarization
Fig.Pipeline architecture of an information extraction process

9. Sentence Segmentation
 Converts raw text into sentences
 List of strings
 Sentence tokenizer
Input Text: John owns a car. It is a Toyota.
Output: Segm1: John owns a car.
Segm2: It is a Toyota.

10. Tokenization
 Identifies the word tokens from given sentence
 Provides a list of tokens as output
 Word tokenizer
Input: John owns a car.
Output: [[John], [owns], [a], [car], [.]]

11. Part of speech tagging (POS Tagging)
 Assigns appropriate part of speech tag to each word
 POS is useful in extraction of nouns, adverbs, adjective, which provide
some meaningful information about text
 Generates a list of tuples with POS annotation

Input: [[John], [owns], [a], [car], [.]]
Output: (NP (NNP John)) (VP (VBZ owns) (NP (DT a) (NN car))) (. .)

12. Entity detection
 Identification of predefined categories such as person, location,
quantities, organizations etc
 NER provides the entity detection for linguistic processing
 NER system uses linguistic grammar-based techniques and also
statistical model to identify the entity
Input: (NP (NNP John)) (VP (VBZ owns) (NP (DT a) (NN car))) (. .)
Output: John->Person

13. Relation detection
 Identifies the possible relation between two or more chunked sentences
 Co-reference chain provides a relation between two or more sentences
 Provides the link between pronouns and its corresponding nouns
 Replacement of the pronouns with proper nouns
Input Text: John owns a car. It is a Toyota. (In form of parse tree)
Output: "a car" -> "a Toyota"; "It" -> "a Toyota"

14. Conclusion
 Automatic Text Summarization has been shown to be useful for Natural
Language Processing tasks such as Question Answering or Text
Classification and other related fields of computer science such as
Information Retrieval. And the access time for information searching will
be improved.

15. Future work
 From our summarization result we have found that by reducing all
sentences that do not contain any geographic information may lead to a
loss of information, since there may exist links between that reduced
sentences. Therefore, we will analyse this issue in detail, by studying
graph based algorithms that capture the relationship between
sentences.

16. References
 https://www.researchgate.net/publication/315667326 Extractive Based Automatic
Text Summarization
 https://github.com/shreyans29/ The semicolon Data Analytics youtube tutorials on The
Semicolon
 https://gist.github.com/shlomibabluki/5473521 summary_tool.py
 https://thetokenizer.com/2013/04/28/build-your-own-summary-tool/
 https://glowingpython.blogspot.in/2014/09/text-summarization-with-nltk.html
 http://www.nltk.org/ NLTK 3.2.5 documentation

17. Thank You !