MuP-SciDocSum: Leveraging Multi-perspective Peer Review Summaries for Scientific Document Summarization

Author:

Kumar Sandeep,Kohli Guneet Singh,Ghosal Tirthankar,Ekbal Asif

Publisher

Springer Nature Singapore

Reference45 articles.

1. Akkasi, A.: Multi perspective scientific document summarization with graph attention networks (GATS). In: Proceedings of the Third Workshop on Scholarly Document Processing, pp. 268–272. Association for Computational Linguistics, Gyeongju (2022). https://aclanthology.org/2022.sdp-1.33

2. Arora, H., Ghosal, T., Kumar, S., Patwal, S., Gooch, P.: INNOVATORS at SemEval-2021 task-11: a dependency parsing and bert-based model for extracting contribution knowledge from scientific papers. In: Palmer, A., Schneider, N., Schluter, N., Emerson, G., Herbelot, A., Zhu, X. (eds.) Proceedings of the 15th International Workshop on Semantic Evaluation, SemEval@ACL/IJCNLP 2021, Virtual Event / Bangkok, Thailand, 5–6 August 2021, pp. 502–510. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.semeval-1.61

3. Auer, S., et al.: Improving access to scientific literature with knowledge graphs. Bibliothek Forschung Praxis 44(3), 516–529 (2020). https://doi.org/10.1515/bfp-2020-2042

4. Cao, M.: A survey on neural abstractive summarization methods and factual consistency of summarization. CoRR abs/2204.09519 (2022). https://doi.org/10.48550/arXiv.2204.09519

5. Cao, Z., Wei, F., Li, S., Li, W., Zhou, M., Wang, H.: Learning summary prior representation for extractive summarization. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 829–833. Association for Computational Linguistics, Beijing (2015). https://doi.org/10.3115/v1/P15-2136, https://aclanthology.org/P15-2136

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