Journal ArticleDOI
A framework for multi-document abstractive summarization based on semantic role labelling
Atif Khan,Naomie Salim,Yogan Jaya Kumar +2 more
- Vol. 30, pp 737-747
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TLDR
Results indicate that the proposed approach performs better than other summarization systems, and the integration of genetic algorithm with SRL based framework for abstractive summarizations results gives improved summarization results.Abstract:
We have proposed a framework for multi-document abstractive summarization based on semantic role labeling (SRL). To the best of our knowledge, SRL has not been employed for abstractive summarization.The integration of genetic algorithm with SRL based framework for abstractive summarization results gives improved summarization results.My study focus on two highlights and discussion is based on these two highlights. We propose a framework for abstractive summarization of multi-documents, which aims to select contents of summary not from the source document sentences but from the semantic representation of the source documents. In this framework, contents of the source documents are represented by predicate argument structures by employing semantic role labeling. Content selection for summary is made by ranking the predicate argument structures based on optimized features, and using language generation for generating sentences from predicate argument structures. Our proposed framework differs from other abstractive summarization approaches in a few aspects. First, it employs semantic role labeling for semantic representation of text. Secondly, it analyzes the source text semantically by utilizing semantic similarity measure in order to cluster semantically similar predicate argument structures across the text; and finally it ranks the predicate argument structures based on features weighted by genetic algorithm (GA). Experiment of this study is carried out using DUC-2002, a standard corpus for text summarization. Results indicate that the proposed approach performs better than other summarization systems.read more
Citations
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Journal ArticleDOI
Recent automatic text summarization techniques: a survey
Mahak Gambhir,Vishal Gupta +1 more
TL;DR: A comprehensive survey of recent text summarization extractive approaches developed in the last decade is presented and the discussion of useful future directions that can help researchers to identify areas where further research is needed are discussed.
Journal ArticleDOI
Automatic text summarization: A comprehensive survey
TL;DR: This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.
Proceedings ArticleDOI
Neural Semantic Role Labeling with Dependency Path Embeddings
Michael Roth,Mirella Lapata +1 more
TL;DR: This paper introduced a neural sequence model for semantic role labeling, which treats such instances as subsequences of lexicalized dependency paths and learns suitable embedding representations for the task of role labeling.
Posted Content
Neural Semantic Role Labeling with Dependency Path Embeddings
Michael Roth,Mirella Lapata +1 more
TL;DR: A novel model for semantic role labeling that makes use of neural sequence modeling techniques and treats complex syntactic structures and related phenomena, such as nested subordinations and nominal predicates, as subsequences of lexicalized dependency paths and learns suitable embedding representations.
Proceedings ArticleDOI
A survey on abstractive text summarization
N. Moratanch,S. Chitrakala +1 more
TL;DR: This survey portrays that most of the abstractive summarization methods produces highly cohesive, coherent, less redundant summary and information rich.
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