Proceedings ArticleDOI
A Survey of Extractive and Abstractive Text Summarization Techniques
Vipul Dalal,Latesh Malik +1 more
- pp 109-110
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TLDR
This paper intends to investigate techniques and methods used by researchers for automatic text summarization, with special attention paid to Bio-inspired methods for text summarizing.Abstract:
The existence of the World Wide Web has caused an information explosion. Readers are overloaded with lengthy text documents where a shorter version would suffice. All computer users, be it professionals or novice users, are particularly affected by this predicament. There exists an urgent need for the discovery of knowledge embedded in digital documents. This paper intends to investigate techniques and methods used by researchers for automatic text summarization. Special attention is paid to Bio-inspired methods for text summarization.read more
Citations
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Neural Abstractive Text Summarization with Sequence-to-Sequence Models
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References
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Using lexical chains for text summarization
Regina Barzilay,Michael Elhadad +1 more
TL;DR: Empirical results on the identification of strong chains and of significant sentences are presented in this paper, and plans to address short-comings are briefly presented.
Proceedings Article
Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions
TL;DR: A novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions that have better agreement with human summaries compared to the baseline extractive method.
Proceedings Article
Automated Text Summarization in SUMMARIST
Eduard Hovy,Chin-Yew Lin +1 more
TL;DR: The system’s architecture is described and details of some of its modules, many of them trained on large corpora of text, are provided.
Proceedings ArticleDOI
Swarm Based Text Summarization
TL;DR: The main purpose of the proposed model is for scoring the sentences, emphasizing on dealing with the text features fairly based on their importance, and creates summaries which are 43% similar to the manually generated summaries, while the summaries produced by Ms Word summarizer are 39% similar.
Extracting Summary Sentences Based on the Document Semantic Graph
TL;DR: The experiments with the DUC 2002 and CAST datasets show that including semantic properties and topological graph properties of logical triples yields statistically significant improvement of the microaverage F1 measure for both the extraction of SOP triples that correspond to the semantic structure of extracts and the extractionof summary sentences.