@inproceedings{mahajan-etal-2021-ibmresearch,
title = "{IBMR}esearch at {MEDIQA} 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization",
author = "Mahajan, Diwakar and
Tsou, Ching-Huei and
Liang, Jennifer J",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.bionlp-1.35",
doi = "10.18653/v1/2021.bionlp-1.35",
pages = "302--310",
abstract = "Although recent advances in abstractive summarization systems have achieved high scores on standard natural language metrics like ROUGE, their lack of factual consistency remains an open challenge for their use in sensitive real-world settings such as clinical practice. In this work, we propose a novel approach to improve factual correctness of a summarization system by re-ranking the candidate summaries based on a factual vector of the summary. We applied this process during our participation in MEDIQA 2021 Task 3: Radiology Report Summarization, where the task is to generate an impression summary of a radiology report, given findings and background as inputs. In our system, we first used a transformer-based encoder-decoder model to generate top N candidate impression summaries for a report, then trained another transformer-based model to predict a 14-observations-vector of the impression based on the findings and background of the report, and finally, utilized this vector to re-rank the candidate summaries. We also employed a source-specific ensembling technique to accommodate for distinct writing styles from different radiology report sources. Our approach yielded 2nd place in the challenge.",
}
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<abstract>Although recent advances in abstractive summarization systems have achieved high scores on standard natural language metrics like ROUGE, their lack of factual consistency remains an open challenge for their use in sensitive real-world settings such as clinical practice. In this work, we propose a novel approach to improve factual correctness of a summarization system by re-ranking the candidate summaries based on a factual vector of the summary. We applied this process during our participation in MEDIQA 2021 Task 3: Radiology Report Summarization, where the task is to generate an impression summary of a radiology report, given findings and background as inputs. In our system, we first used a transformer-based encoder-decoder model to generate top N candidate impression summaries for a report, then trained another transformer-based model to predict a 14-observations-vector of the impression based on the findings and background of the report, and finally, utilized this vector to re-rank the candidate summaries. We also employed a source-specific ensembling technique to accommodate for distinct writing styles from different radiology report sources. Our approach yielded 2nd place in the challenge.</abstract>
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%0 Conference Proceedings
%T IBMResearch at MEDIQA 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization
%A Mahajan, Diwakar
%A Tsou, Ching-Huei
%A Liang, Jennifer J.
%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 mahajan-etal-2021-ibmresearch
%X Although recent advances in abstractive summarization systems have achieved high scores on standard natural language metrics like ROUGE, their lack of factual consistency remains an open challenge for their use in sensitive real-world settings such as clinical practice. In this work, we propose a novel approach to improve factual correctness of a summarization system by re-ranking the candidate summaries based on a factual vector of the summary. We applied this process during our participation in MEDIQA 2021 Task 3: Radiology Report Summarization, where the task is to generate an impression summary of a radiology report, given findings and background as inputs. In our system, we first used a transformer-based encoder-decoder model to generate top N candidate impression summaries for a report, then trained another transformer-based model to predict a 14-observations-vector of the impression based on the findings and background of the report, and finally, utilized this vector to re-rank the candidate summaries. We also employed a source-specific ensembling technique to accommodate for distinct writing styles from different radiology report sources. Our approach yielded 2nd place in the challenge.
%R 10.18653/v1/2021.bionlp-1.35
%U https://aclanthology.org/2021.bionlp-1.35
%U https://doi.org/10.18653/v1/2021.bionlp-1.35
%P 302-310
Markdown (Informal)
[IBMResearch at MEDIQA 2021: Toward Improving Factual Correctness of Radiology Report Abstractive Summarization](https://aclanthology.org/2021.bionlp-1.35) (Mahajan et al., BioNLP 2021)
ACL