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    <abstract>In this article, we describe our systems for the MEDIQA 2021 Shared Tasks. First, we will describe our method for the second task, Multi-Answer Summarization (MAS). For extractive summarization, two series of methods are applied. The first one follows (CITATION). First a RoBERTa model is first applied to give a local ranking of the candidate sentences. Then a Markov Chain model is applied to evaluate the sentences globally. The second method applies cross-sentence contextualization to improve the local ranking and discard the global ranking step. Our methods achieve the 1st Place in the MAS task. For the question summarization (QS) and radiology report summarization (RRS) tasks, we explore how end-to-end pre-trained seq2seq model perform. A series of tricks for improving the fine-tuning performances are validated.</abstract>
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%0 Conference Proceedings
%T paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization
%A Zhu, Wei
%A He, Yilong
%A Chai, Ling
%A Fan, Yunxiao
%A Ni, Yuan
%A Xie, Guotong
%A Wang, Xiaoling
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 20th Workshop on Biomedical Language Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F zhu-etal-2021-paht
%X In this article, we describe our systems for the MEDIQA 2021 Shared Tasks. First, we will describe our method for the second task, Multi-Answer Summarization (MAS). For extractive summarization, two series of methods are applied. The first one follows (CITATION). First a RoBERTa model is first applied to give a local ranking of the candidate sentences. Then a Markov Chain model is applied to evaluate the sentences globally. The second method applies cross-sentence contextualization to improve the local ranking and discard the global ranking step. Our methods achieve the 1st Place in the MAS task. For the question summarization (QS) and radiology report summarization (RRS) tasks, we explore how end-to-end pre-trained seq2seq model perform. A series of tricks for improving the fine-tuning performances are validated.
%R 10.18653/v1/2021.bionlp-1.10
%U https://aclanthology.org/2021.bionlp-1.10
%U https://doi.org/10.18653/v1/2021.bionlp-1.10
%P 96-102

Markdown (Informal)

[paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization](https://aclanthology.org/2021.bionlp-1.10) (Zhu et al., BioNLP 2021)

• paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization (Zhu et al., BioNLP 2021)

ACL

• Wei Zhu, Yilong He, Ling Chai, Yunxiao Fan, Yuan Ni, Guotong Xie, and Xiaoling Wang. 2021. paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization. In Proceedings of the 20th Workshop on Biomedical Language Processing, pages 96–102, Online. Association for Computational Linguistics.