Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization

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Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization
Title:
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization
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EMNLP 2021
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Publication Date:
07 November 2021
Citation:
Yong Guan, Shaoru Guo, Ru Li, Xiaoli Li and Hongye Tan, Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization, EMNLP 2021
Abstract:
Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization (FS3), which leverages Frame semantics to model sentences from both intra sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that FS3 model outperforms six state-of-the-art methods significantly.
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Publisher Copyright
Funding Info:
This research is supported by core funding from: Institute for Infocomm Research
Grant Reference no. : Nil
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