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A framework for multi-document abstractive summarization based on semantic role labelling

Khan, Atif and Salim, Naomie and Jaya Kumar, Yogan (2015) A framework for multi-document abstractive summarization based on semantic role labelling. Applied Soft Computing Journal, 30 . pp. 737-747. ISSN 1568-4946

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Official URL: http://dx.doi.org/10.1016/j.asoc.2015.01.070

Abstract

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.

Item Type:Article
Uncontrolled Keywords:genetic algorithm, language generation, semantic role labeling
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:55455
Deposited By: Practical Student
Deposited On:08 Sep 2016 06:37
Last Modified:15 Feb 2017 04:59

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