Automatic Text Summarization

Mar 23, 20150 likes870 views

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This document discusses text summarization using machine learning. It begins by defining text summarization as reducing a text to create a summary that retains the most important points. There are two main types: single document summarization and multiple document summarization. Extractive summarization creates summaries by extracting phrases or sentences from the source text, while abstractive summarization expresses ideas using different words. Supervised machine learning approaches use labeled training data to train classifiers to select content, while unsupervised approaches select content based on metrics like term frequency-inverse document frequency. ROUGE is commonly used to automatically evaluate summaries by comparing them to human references. Query-focused multi-document summarization aims to answer a user's information need by summarizing relevant documents

Text SummarizationCarlos Castillo (ChaTo)

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The document summarizes different techniques for automatic document summarization including extractive and abstractive approaches. It discusses simple techniques like frequency-based methods and cue phrases. Graph-based approaches like TextRank and LexRank that model text as a graph are explained. Linguistic methods involving lexical chains and rhetorical structure are covered. Finally, it summarizes WordNet-based semantic approaches and techniques for evaluating summaries.

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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.

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The project is developed as a part of IRE course work @IIIT-Hyderabad. Team members: Aishwary Gupta (201302216) B Prabhakar (201505618) Sahil Swami (201302071) Links: https://github.com/prabhakar9885/Text-Summarization http://prabhakar9885.github.io/Text-Summarization/ https://www.youtube.com/playlist?list=PLtBx4kn8YjxJUGsszlev52fC1Jn07HkUw http://www.slideshare.net/prabhakar9885/text-summarization-60954970 https://www.dropbox.com/sh/uaxc2cpyy3pi97z/AADkuZ_24OHVi3PJmEAziLxha?dl=0

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