paht_nlp @ MEDIQA 2021: Multi-grained Query Focused Multi-Answer Summarization

  • Wei Zhu*
  • , Yilong He
  • , Ling Chai
  • , Yunxiao Fan
  • , Yuan Ni
  • , Guotong Xie
  • , Xiaoling Wang
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

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 Xu and Lapata (2020). 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.

Original languageEnglish
Title of host publicationProceedings of the 20th Workshop on Biomedical Language Processing, BioNLP 2021
EditorsDina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
PublisherAssociation for Computational Linguistics (ACL)
Pages96-102
Number of pages7
ISBN (Electronic)9781954085404
StatePublished - 2021
Event20th Workshop on Biomedical Language Processing, BioNLP 2021 - Virtual, Online
Duration: 11 Jun 2021 → …

Publication series

NameProceedings of the 20th Workshop on Biomedical Language Processing, BioNLP 2021

Conference

Conference20th Workshop on Biomedical Language Processing, BioNLP 2021
CityVirtual, Online
Period11/06/21 → …

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