Medical Question Summarization with Entity-driven Contrastive Learning Article Swipe
Sibo Wei
,
Wenpeng Lü
,
Xueping Peng
,
Shoujin Wang
,
Yifei Wang
,
Weiyu Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.07437
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.07437
By summarizing longer consumer health questions into shorter and essential ones, medical question-answering systems can more accurately understand consumer intentions and retrieve suitable answers. However, medical question summarization is very challenging due to obvious distinctions in health trouble descriptions from patients and doctors. Although deep learning has been applied to successfully address the medical question summarization (MQS) task, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance. To address these challenges, this paper proposes a novel medical question summarization framework based on entity-driven contrastive learning (ECL). ECL employs medical entities present in frequently asked questions (FAQs) as focuses and devises an effective mechanism to generate hard negative samples. This approach compels models to focus on essential information and consequently generate more accurate question summaries. Furthermore, we have discovered that some MQS datasets, such as the iCliniq dataset with a 33% duplicate rate, have significant data leakage issues. To ensure an impartial evaluation of the related methods, this paper carefully examines leaked samples to reorganize more reasonable datasets. Extensive experiments demonstrate that our ECL method outperforms the existing methods and achieves new state-of-the-art performance, i.e., 52.85, 43.16, 41.31, 43.52 in terms of ROUGE-1 metric on MeQSum, CHQ-Summ, iCliniq, HealthCareMagic dataset, respectively. The code and datasets are available at https://github.com/yrbobo/MQS-ECL.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2304.07437
- https://arxiv.org/pdf/2304.07437
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366341512
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4366341512Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2304.07437Digital Object Identifier
- Title
-
Medical Question Summarization with Entity-driven Contrastive LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-04-15Full publication date if available
- Authors
-
Sibo Wei, Wenpeng Lü, Xueping Peng, Shoujin Wang, Yifei Wang, Weiyu ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2304.07437Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2304.07437Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2304.07437Direct OA link when available
- Concepts
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Automatic summarization, Computer science, Focus (optics), Information retrieval, Artificial intelligence, Data science, Natural language processing, Machine learning, Physics, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.contrastive | 98 |
| abstract_inverted_index.demonstrate | 184 |
| abstract_inverted_index.experiments | 183 |
| abstract_inverted_index.information | 131 |
| abstract_inverted_index.outperforms | 189 |
| abstract_inverted_index.significant | 158 |
| abstract_inverted_index.summarizing | 1 |
| abstract_inverted_index.Furthermore, | 139 |
| abstract_inverted_index.consequently | 133 |
| abstract_inverted_index.descriptions | 38 |
| abstract_inverted_index.distinctions | 34 |
| abstract_inverted_index.performance, | 197 |
| abstract_inverted_index.performance. | 81 |
| abstract_inverted_index.successfully | 50 |
| abstract_inverted_index.entity-driven | 97 |
| abstract_inverted_index.respectively. | 214 |
| abstract_inverted_index.summarization | 27, 55, 93 |
| abstract_inverted_index.HealthCareMagic | 212 |
| abstract_inverted_index.state-of-the-art | 196 |
| abstract_inverted_index.question-answering | 12 |
| abstract_inverted_index.https://github.com/yrbobo/MQS-ECL. | 222 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |