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Exploiting comments information to improve legal public opinion news abstractive summarization

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Abstract

Automatically generating a brief summary for legal-related public opinion news (LPO-news, which contains legal words or phrases) plays an important role in rapid and effective public opinion disposal. For LPO-news, the critical case elements which are significant parts of the summary may be mentioned several times in the reader comments. Consequently, we investigate the task of comment-aware abstractive text summarization for LPO-news, which can generate salient summary by learning pivotal case elements from the reader comments. In this paper, we present a hierarchical comment-aware encoder (HCAE), which contains four components: 1) a traditional sequenceto-sequence framework as our baseline; 2) a selective denoising module to filter the noisy of comments and distinguish the case elements; 3) a merge module by coupling the source article and comments to yield comment-aware context representation; 4) a recoding module to capture the interaction among the source article words conditioned on the comments. Extensive experiments are conducted on a large dataset of legal public opinion news collected from micro-blog, and results show that the proposed model outperforms several existing state-of-the-art baseline models under the ROUGE metrics.

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References

  1. Nallapati R, Zhou B W, Santos D C, Guçehre Ç, Xiang B. Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. 2016, 280–290

  2. Gu J T, Lu Z D, Li H, Li V O. Incorporating copying mechanism in sequence-to-sequence learning. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1631–1640

  3. Zhou Q Y, Yang N, Wei F R, Zhou M. Selective encoding for abstractive sentence summarization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017, 1095–1104

  4. Xu H Y, Wang Z Q, Zhang Y F, Weng X L, Wang Z J, Zhou G D. Document structure model for survey generation using neural network. Frontiers of Computer Science, 2021, 15(4): 1–10

    Article  Google Scholar 

  5. Jadhav A, Rajan V. Extractive summarization with SWAP-NET: Sentences and words from alternating pointer networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 142–151

  6. Wang H, Wang X, Xiong W H, Yu M, Guo X X, Chang S Y, Wang W Y. Self-supervised learning for contextualized extractive summarization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019, 2221–2227

  7. Cho S W, Lebanoff L, Foroosh H, Liu F. Improving the similarity measure of determinantal point processes for extractive multi-document summarization. 2019, arXiv preprint arXiv: 1906.00072

  8. Zhao W X, Wen J R, Li X M. Generating timeline summaries with social media attention. Frontiers of Computer Science, 2016, 10(4): 702–716

    Article  Google Scholar 

  9. Rush A M, Chopra S, Weston J. A neural attention model for abstractive sentence summarization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015, 379–389

  10. Vinyals O, Fortunato M, Jaitly N. Pointer networks. Advances in neural information processing systems, 2015, 2692–2700

  11. See A, Liu P J, Manning C D. 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). 2017, 1073–1083

  12. Song K Q, Zhao L, Liu F. Structure-infused copy mechanisms for abstractive summarization. 2018, arXiv preprint arXiv: 1806.05658

  13. Zhang X X, Lapata M. Sentence simplification with deep reinforcement learning. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017, 584–594

  14. Pasunuru R, Bansal M. Multi-reward reinforced summarization with saliency and entailment. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). 2018, 646–653

  15. Zeng W Y, Luo W J, Fidler S, Urtasun R. Efficient summarization with read-again and copy mechanism. 2016, arXiv preprint arXiv: 1611.03382

  16. Xia Y C, Tian F, Wu L J, Lin J X, Qin T, Yu N H, Liu T Y. Deliberation networks: Sequence generation beyond one-pass decoding. Advances in Neural Information Processing Systems, 2017, 1784–1794

  17. Chen Y C, Bansal M. Fast abstractive summarization with reinforce-selected sentence rewriting. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 675–686

  18. Hsu W T, Lin C K, Lee M Y, Min K R, Tang J, Sun M. A unified model for extractive and abstractive summarization using inconsistency loss. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, 132–141

  19. Hu M S, Sun A X, Lim E P. Comments-oriented document summarization: understanding documents with readers’ feedback. In: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 2008, 291–298

  20. Yang Z, Cai K K, Tang J, Zhang L, Su Z, Li J Z. Social context summarization. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. 2011, 255–264

  21. Nguyen M T, Tran C X, Tran D V, Nguyen M L. Solscsum: A linked sentence-comment dataset for social context summarization. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. 2016, 2409–2412

  22. Nguyen M T, Lai D V, Do P K, Tran D V, Le Nguyen M. Vsolscsum: Building a vietnamese sentence-comment dataset for social context summarization. In: Proceedings of the 12th Workshop on Asian Language Resources (ALR12). 2016, 38–48

  23. Li P J, Bing L D, Lam W, Li H, Liao Y. Reader-aware multi-document summarization via sparse coding. In: Proceedings of Twenty-Fourth International Joint Conference on Artificial Intelligence. 2015

  24. Li P J, Bing L D, Lam W. Reader-aware multi-document summarization: An enhanced model and the first dataset. In: Proceedings of the Workshop on New Frontiers in Summarization. 2017, 91–99

  25. Gao S, Chen X Y, Li P J, Ren Z C, Bing L D, Zhao D Y, Yan R. Abstractive text summarization by incorporating reader comments. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 6399–6406

  26. Gao S, Chen X Y, Ren Z C, Zhao D Y, Yan R. From standard summarization to new tasks and beyond: Summarization with manifold information. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. 2020, 4854–4860

  27. Bhattacharya P, Hiware K, Rajgaria S, Pochhi N, Ghosh K, Ghosh S. A comparative study of summarization algorithms applied to legal case judgments. In: Proceedings of European Conference on Information Retrieval. 2019, 413–428

  28. Jain D, Borah M D, Biswas A. Summarization of legal documents: Where are we now and the way forward. Computer Science Review, 2021, 40: 100388

    Article  Google Scholar 

  29. Hachey B, Grover C. Extractive summarisation of legal texts. Artificial Intelligence and Law, 2006, 14(4): 305–345

    Article  Google Scholar 

  30. Kumar R, Raghuveer K. Legal document summarization using latent dirichlet allocation. Int. J. of Computer Science and Telecommunications, 2012, 3: 114–117

    Google Scholar 

  31. Galgani F, Compton P, Hoffmann A. Combining different summarization techniques for legal text. In: Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data. 2012, 115–123

  32. Acharya H R, Bhat A D, Avinash K, Srinath R. Legonet-classification and extractive summarization of indian legal judgments with capsule networks and sentence embeddings. Journal of Intelligent & Fuzzy Systems, 2020(Preprint): 1–10

  33. Elnaggar A, Gebendorfer C, Glaser I, Matthes F. Multi-task deep learning for legal document translation, summarization and multi-label classification. In: Proceedings of the 2018 Artificial Intelligence and Cloud Computing Conference. 2018, 9–15

  34. Manor L, Li J J. Plain English summarization of contracts. In: Proceedings of the Natural Legal Language Processing Workshop 2019. 2019, 1–11

  35. Han P Y, Gao S X, Yu Z T, Huang Y X, Guo J J. Case-involved public opinion news summarization with case elements guidance. Journal of Chinese Information Processing, 2020, 34(5): 56–63

    Google Scholar 

  36. Huang Y X, Yu Z T, Guo J J, Yu Z Q, Xian Y T. Legal public opinion news abstractive summarization by incorporating topic information. International Journal of Machine Learning and Cybernetics, 2020: 1–12

  37. Hochreiter S, Schmidhuber J. Lstm can solve hard long time lag problems. Advances in neural information processing systems, 1997, 473–479

  38. Wang K, Quan X J, Wang R. BiSET: Bi-directional selective encoding with template for abstractive summarization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019, 2153–2162

  39. Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2014, 655–665

  40. Seo M, Kembhavi A, Farhadi A, Hajishirzi H. Bidirectional attention flow for machine comprehension. 2016, arXiv preprint arXiv: 1611.01603

  41. Gulcehre C, Ahn S, Nallapati R, Zhou B W, Bengio Y. Pointing the unknown words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 140–149

  42. Zhang Y, Yu Z T, Mao C L, Huang Y X, Gao S X. Correlation analysis of law-related news combining bidirectional attention flow of news title and body. Journal of Intelligent & Fuzzy Systems, (Preprint): 1–13

  43. Lin C Y. ROUGE: A package for automatic evaluation of summaries. Text Summarization Branches Out, 2004, 74–81

  44. Adam P, Sam G, Soumith C, Gregory C, Edward Y, Zachary D, Ze-Ming L, Alban D, Luca A, Adam L. Automatic differentiation in pytorch. In: Proceedings of Neural Information Processing Systems. 2017

  45. Hu Z K, Li X, Tu C C, Liu Z Y, Sun M S. Few-shot charge prediction with discriminative legal attributes. In: Proceedings of the 27th International Conference on Computational Linguistics. 2018, 487–498

  46. Kingma D P, Ba J. Adam: A method for stochastic optimization. 2014, arXiv preprint arXiv: 1412.6980

  47. Lin J Y, Sun X, Ma S M, Su Q. Global encoding for abstractive summarization. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2018, 163–169

  48. Xu W R, Li C L, Lee M H, Zhang C. Multi-task learning for abstractive text summarization with key information guide network. EURASIP Journal on Advances in Signal Processing, 2020, 2020: 1–11

    Article  Google Scholar 

  49. Li H R, Zhu J N, Zhang J J, Zong C Q, He X D. Keywords-guided abstractive sentence summarization. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 8196–8203

  50. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I. Attention is all you need. Advances in Neural Information Processing Systems 30, 2017, 5998–6008

    Google Scholar 

  51. Klein G, Kim Y, Deng Y T, Nguyen V, Senellart J, Rush A. OpenNMT: Neural machine translation toolkit. In: Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Papers). 2018, 177–184

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018YFC0830105, 2018YFC 0830101, 2018YFC0830100); the National Natural Science Foundation of China (Grant Nos. 61972186, 61762056, 61472168); the Yunnan Provincial Major Science and Technology Special Plan Projects (202002AD080001); the General Projects of Basic Research in Yunnan Province (202001AT0 70046, 202001AT070047).

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Authors and Affiliations

  1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Yuxin Huang, Zhengtao Yu, Yan Xiang, Zhiqiang Yu & Junjun Guo

  2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, 650500, China

    Yuxin Huang, Zhengtao Yu, Yan Xiang, Zhiqiang Yu & Junjun Guo

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  1. Yuxin Huang
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  2. Zhengtao Yu
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  3. Yan Xiang
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Corresponding author

Correspondence to Zhengtao Yu.

Additional information

Yuxin Huang is a PhD candidate in computer science at Kunming university of Science and Technology, China. His research interests include natural language processing, text summarization, machine translation, etc.

Zhengtao Yu received the PhD degree in computer application technology from Beijing Institute of Technology, China in 2005. Now he is a professor and PhD supervisor at Kunming University of Science and Technology, China and the director of Yunnan Key Laboratory of Artificial Intelligence. His research interests include natural language processing, machine translation and information retrieval, etc.

Yan Xiang received the MS degree from Wuhan University, China in 2001. She is currently a PhD candidate in computer science at Kunming University of Science and Technology, China. Her research interests include medical image processing, natural language processing, sentiment classification, and text mining, etc.

Zhiqiang Yu is a PhD candidate in computer science at Kunming university of Science and Technology, China. His research interests include natural language processing, neural machine translation, etc.

Junjun Guo received the PhD degree from Xi’an Jiao Tong University, China in 2016. Now He is an associate professor at Kunming University of Science and Technology, China. His research interests include natural language processing, machine translation, etc.

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Huang, Y., Yu, Z., Xiang, Y. et al. Exploiting comments information to improve legal public opinion news abstractive summarization. Front. Comput. Sci. 16, 166333 (2022). https://doi.org/10.1007/s11704-021-0561-z

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