Mao, Q, Li, J, Peng, H et al. (4 more authors) (2022) Fact-Driven Abstractive Summarization by Utilizing Multi-Granular Multi-Relational Knowledge. IEEE/ACM Transactions on Audio Speech and Language Processing, 30. pp. 1665-1678. ISSN 2329-9290
Abstract
Abstractive summarization generates a concise summary to capture the key ideas of the source text. This task underpins important applications like information retrieval, document comprehension, and event tracking. While much progress has been achieved, state-of-the-art summarization approaches often fail to generate high-quality summaries to reproduce factual details accurately. One of the key limitations of existing solutions is that they are primarily concerned about extracting facts from the source text but overlook other crucial factual information, such as the related time, locations, reasons, consequences, purposes, participants and involved parties. Furthermore, the current summarization frameworks are inadequate in modeling the complex semantic relations among facts and the corresponding factual information, leaving much room for improvement. This paper presents FFSum , a novel summarization framework for exploiting multi-grained factual information to improve text summarization. To this end, FFSum constructs an individual fine-grained factual graph with multiple relations among facts and the corresponding factual information. It employs a fact-driven graph attention network to integrate multi-granular factual representations at the encoding stage. It then uses a hybrid pointer network to retrieve factual pieces from the graph for the summary generation. We evaluate the FFSum by applying it to two real-world datasets. Experimental results show that the FFSum consistently outperforms a state-of-the-art approach across evaluation datasets.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Fact consistency, graph neural network, language model, pointer network, text summarization |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Mar 2022 14:24 |
Last Modified: | 30 Mar 2023 02:35 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TASLP.2022.3161157 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184504 |