Query-Based Dialogue Summarization Using BART

Research Article
Open access

Query-Based Dialogue Summarization Using BART

Lingxiao Du 1*
  • 1 Dalian Maritime University    
  • *corresponding author 851448523@qq.com
Published on 22 January 2024 | https://doi.org/10.54254/2755-2721/29/20231149
ACE Vol.29
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-259-6
ISBN (Online): 978-1-83558-260-2

Abstract

Conversation summarisation is the transformation of long conversational texts into concise and accurate summaries, the importance of which lies in improving the user experience and information filtering. As an important natural language processing task, conversation summarisation can provide concise and accurate information and avoid repetition and redundancy. In the dialogue summarisation task, pre-trained language models can be used to summarise long conversations and generate concise and accurate summaries. The aim of this paper is to investigate the possibility of using bidirectional and auto-regressive transformer models for dialogue summarisation tasks. In our experiments, we analysed the characteristics of the Query-based Multi-domain Meeting Summarization (QMsum) dialogue summarisation dataset, proposed a dialogue summarisation model based on the Bidirectional and Auto-Regressive Transformer model, and designed evaluation experiments to compare its performance with other methods in the dialogue summarisation task. The experimental results show that the results of this thesis are important for facilitating the development of dialogue summarisation tasks and the application of the Bidirectional and Auto-Regressive Transformer model.

Keywords:

conversation summary, PTML model, BART model, QMsum dataset

Du,L. (2024). Query-Based Dialogue Summarization Using BART. Applied and Computational Engineering,29,160-166.
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References

[1]. Feng X, Feng X, Qin B. A survey on dialogue summarization: Recent advances and new frontiers. arXiv preprint arXiv:2107.03175. 2021 Jul 7.

[2]. Chen Y, Liu Y, Chen L, Zhang Y. DialogSum: A real-life scenario dialogue summarization dataset. arXiv preprint arXiv:2105.06762. 2021 May 14.

[3]. Zhu C, Liu Y, Mei J, Zeng M. MediaSum: A large-scale media interview dataset for dialogue summarization. arXiv preprint arXiv:2103.06410. 2021 Mar 11.

[4]. Zhang Y, Ni A, Yu T, Zhang R, Zhu C, Deb B, Celikyilmaz A, Awadallah AH, Radev D. An exploratory study on long dialogue summarization: What works and what's next. arXiv preprint arXiv:2109.04609. 2021 Sep 10.

[5]. Zhao, L., Xu, W., & Guo, J. (2020, December). Improving abstractive dialogue summarization with graph structures and topic words. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 437-449).

[6]. Gliwa, B., Mochol, I., Biesek, M., & Wawer, A. (2019). SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization. arXiv preprint arXiv:1911.12237.

[7]. Feng, X., Feng, X., Qin, L., Qin, B., & Liu, T. (2021). Language model as an annotator: Exploring DialoGPT for dialogue summarization. arXiv preprint arXiv:2105.12544.

[8]. Zhong, M., Da Y., Tao Y., Zaidi A., Mutuma M., Jha R., Awadallah A.H., Celikyilmaz A., Liu Y., Qiu X. Radev D. "QMSum: A new benchmark for query-based multi-domain meeting summarization." arXiv preprint arXiv:2104.05938 (2021).

[9]. Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. 2019 Oct 29.

[10]. Lundberg, C., Viñuela, L. S., & Biales, S. (2022, July). Dialogue Summarization using BART. In Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges (pp. 121-125).

[11]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

Cite this article

Du,L. (2024). Query-Based Dialogue Summarization Using BART. Applied and Computational Engineering,29,160-166.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-259-6(Print) / 978-1-83558-260-2(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.29
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Feng X, Feng X, Qin B. A survey on dialogue summarization: Recent advances and new frontiers. arXiv preprint arXiv:2107.03175. 2021 Jul 7.

[2]. Chen Y, Liu Y, Chen L, Zhang Y. DialogSum: A real-life scenario dialogue summarization dataset. arXiv preprint arXiv:2105.06762. 2021 May 14.

[3]. Zhu C, Liu Y, Mei J, Zeng M. MediaSum: A large-scale media interview dataset for dialogue summarization. arXiv preprint arXiv:2103.06410. 2021 Mar 11.

[4]. Zhang Y, Ni A, Yu T, Zhang R, Zhu C, Deb B, Celikyilmaz A, Awadallah AH, Radev D. An exploratory study on long dialogue summarization: What works and what's next. arXiv preprint arXiv:2109.04609. 2021 Sep 10.

[5]. Zhao, L., Xu, W., & Guo, J. (2020, December). Improving abstractive dialogue summarization with graph structures and topic words. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 437-449).

[6]. Gliwa, B., Mochol, I., Biesek, M., & Wawer, A. (2019). SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization. arXiv preprint arXiv:1911.12237.

[7]. Feng, X., Feng, X., Qin, L., Qin, B., & Liu, T. (2021). Language model as an annotator: Exploring DialoGPT for dialogue summarization. arXiv preprint arXiv:2105.12544.

[8]. Zhong, M., Da Y., Tao Y., Zaidi A., Mutuma M., Jha R., Awadallah A.H., Celikyilmaz A., Liu Y., Qiu X. Radev D. "QMSum: A new benchmark for query-based multi-domain meeting summarization." arXiv preprint arXiv:2104.05938 (2021).

[9]. Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461. 2019 Oct 29.

[10]. Lundberg, C., Viñuela, L. S., & Biales, S. (2022, July). Dialogue Summarization using BART. In Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges (pp. 121-125).

[11]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.

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