ABSTRACT
Automatic text summtarization technology is a crucial component in the field of natural language processing, utilized to address the demands of processing extensive textual data by effectively extracting key information to enhance task efficiency. With the rise of pretrained language models, abstract text summarization has progressively become mainstream, producing fluent srummaries that encapsulate core content. Nonetheless, abstract text summarization unavoidably faces problems of inconsistency with the original text. This paper introduces a sequence tagging task to achieve multi-task learning for abstract text summarization models. In this sequence tagging task, we meticulously designed annotated datasets at both entity and sentence levels based on an analysis of the XSum dataset, aiming to enhance the factual consistency of generated summaries. Experimental results demonstrate that the optimized BART model yields favorable performance in terms of ROUGE and FactCC metrics.
- Wafaa S. El-Kassas, Cherif R. Salama, Ahmed A. Rafea, and Hoda K. Mohamed. 2021. Automatic text summarization: A comprehensive survey. Expert Systems with Applications. 165, 113679. https://doi.org/10.1016/j.eswa.2020.113679.Google ScholarCross Ref
- Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, and Chandan K. Reddy. 2021. Neural Abstractive Text Summarization with Sequence-to-Sequence Models. 2, 1, 1-37. https://doi.org/10.1145/3419106.Google ScholarDigital Library
- Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get To The Point: Summarization with Pointer-Generator Networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, 1073–1083. https://doi.org/10.18653/v1/P17-1099.Google ScholarCross Ref
- Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 7871–7880. https://doi.org/10.18653/v1/2020.acl-main.703.Google ScholarCross Ref
- Zheng Zhao, Shay B. Cohen, and Bonnie Webber. 2020. Reducing Quantity Hallucinations in Abstractive Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, Online, 2237–2249. https://doi.org/10.18653/v1/2020.findings-emnlp.203.Google ScholarCross Ref
- Sihao Chen, Fan Zhang, Kazoo Sone, and Dan Roth. 2021. Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 5935–5941. https://doi.org/10.18653/v1/2021.naacl-main.475.Google ScholarCross Ref
- Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, and Meng Jiang. 2021. Enhancing Factual Consistency of Abstractive Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, 718–733. https://doi.org/10.18653/v1/2021.naacl-main.58.Google ScholarCross Ref
- Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020. Asking and Answering Questions to Evaluate the Factual Consistency of Summaries. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 5008–5020. https://doi.org/10.18653/v1/2020.acl-main.450.Google ScholarCross Ref
- Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020. Evaluating the Factual Consistency of Abstractive Text Summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 9332–9346. https://doi.org/10.18653/v1/2020.emnlp-main.750.Google ScholarCross Ref
- Shuyang Cao and Lu Wang. 2021. CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 6633–6649. https://doi.org/10.18653/v1/2021.emnlp-main.532.Google ScholarCross Ref
- Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To appear.Google Scholar
- Rada Mihalcea and Paul Tarau. 2004. TextRank: Bringing Order into Text. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Barcelona, Spain, 404–411.Google Scholar
- Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018. Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 1797–1807. https://doi.org/10.18653/v1/D18-1206.Google ScholarCross Ref
- Chin-Yew Lin. 2004. ROUGE: A Package for Automatic Evaluation of Summaries. In Text Summarization Branches Out. Association for Computational Linguistics, Barcelona, Spain, 74–81.Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186. https://doi.org/10.18653/v1/N19-1423.Google ScholarCross Ref
Index Terms
- Optimization of the Abstract Text Summarization Model Based on Multi-Task Learning
Recommendations
Research on Multi-document Summarization Based on LDA Topic Model
IHMSC '14: Proceedings of the 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics - Volume 02Compared with VSM (Vector Space Model) and graph-ranking models, LDA (Latent Dirichlet Allocation) Model can discover latent topics in the corpus and latent topics are beneficial to use sentence-ranking mechanisms to form a good summary. In the paper, ...
Multi-document Hyperedge-based Ranking for Text Summarization
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge ManagementIn a multi-document settings, graph-based extractive summarization approaches build a similarity graph out of sentences in each cluster of documents then use graph centrality approaches to measure the importance of sentences. The similarity is computed ...
Latent dirichlet allocation based multi-document summarization
AND '08: Proceedings of the second workshop on Analytics for noisy unstructured text dataExtraction based Multi-Document Summarization Algorithms consist of choosing sentences from the documents using some weighting mechanism and combining them into a summary. In this article we use Latent Dirichlet Allocation to capture the events being ...
Comments