Wenpeng Lu
Skip Abstract Section
Abstract
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 article proposes a novel medical question summarization framework based on
- [1] . 2019. On the summarization of consumer health questions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL’19). 2228–2234.Google Scholar
Cross Ref
- [2] . 2021. Overview of the MEDIQA 2021 shared task on summarization in the medical domain. In Proceedings of the 20th Workshop on Biomedical Language Processing (BioNLP’21). 74–85.Google ScholarCross Ref
- [3] . 2019. A question-entailment approach to question answering. BMC Bioinformatics 20, 1 (2019), 1–23.Google Scholar
- [4] . 2022. Long context question answering via supervised contrastive learning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22). 2872–2879.Google ScholarCross Ref
- [5] . 2021. CLIFF: Contrastive learning for improving faithfulness and factuality in abstractive summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP’21). 6633–6649.Google ScholarCross Ref
- [6] . 2022. An information minimization based contrastive learning model for unsupervised sentence embeddings learning. In Proceedings of the 29th International Conference on Computational Linguistics (COLING’22). 4821–4831.Google Scholar
- [7] . 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML’20). 1597–1607.Google ScholarDigital Library
- [8] . 2022. CONTaiNER: Few-shot named entity recognition via contrastive learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL’22). 6338–6353.Google ScholarCross Ref
- [9] . 2021. SimCSE: Simple contrastive learning of sentence embeddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP’21). 6894–6910.Google ScholarCross Ref
- [10] . 2006. Dimensionality reduction by learning an invariant mapping. In Proceedings of the 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’06). 1735–1742.Google ScholarDigital Library
- [11] . 2020. Momentum contrast for unsupervised visual representation learning. In Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’20). 9729–9738.Google ScholarCross Ref
- [12] . 2022. COPNER: Contrastive learning with prompt guiding for few-shot named entity recognition. In Proceedings of the 29th International Conference on Computational Linguistics (COLING’22). 2515–2527.Google Scholar
- [13] . 2020. A survey on contrastive self-supervised learning. Technologies (2020), 9, 1 (2020), 2.Google ScholarCross Ref
- [14] . 2023. Abstractive summarization of text document in Malayalam language: Enhancing attention model using POS tagging feature. ACM Transactions on Asian and Low-Resource Language Information Processing 22, 2 (2023), 1–14.Google ScholarDigital Library
- [15] . 2023. Methodical systematic review of abstractive summarization and natural language processing models for biomedical health informatics: Approaches, metrics and challenges. ACM Transactions on Asian and Low-Resource Language Information Processing (2023), 1–37.Google ScholarDigital Library
- [16] . 2018. Semantic annotation of consumer health questions. BMC Bioinformatics 19, 1 (2018), 1–28.Google ScholarCross Ref
- [17] . 2020. Expanding query answers on medical knowledge bases. In Proceedings of the 2020 International Conference on Extending Database Technology (EDBT’20). 567–578.Google Scholar
- [18] . 2020. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL’20). 7871–7880.Google ScholarCross Ref
- [19] . 2022. Improving word translation via two-stage contrastive learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL’22). 4353–4374.Google ScholarCross Ref
- [20] . 2004. ROUGE: A package for automatic evaluation of summaries. In Proceedings of the Text Summarization Branches Out. 74–81.Google Scholar
- [21] . 2021. SimCLS: A simple framework for contrastive learning of abstractive summarization. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP’21). 1065–1072.Google ScholarCross Ref
- [22] . 2022. BRIO: Bringing order to abstractive summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL’22). 2890–2903.Google ScholarCross Ref
- [23] . 2022. Multi-document summarization via deep learning techniques: A survey. ACM Computing Surveys 55, 5 (2022), 1–37.Google ScholarDigital Library
- [24] . 2021. Joint summarization-entailment optimization for consumer health question understanding. In Proceedings of the 2nd Workshop on Natural Language Processing for Medical Conversations (NLPMC’21). 58–65.Google ScholarCross Ref
- [25] . 2021. A gradually soft multi-task and data-augmented approach to medical question understanding. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP’21). 1505–1515.Google ScholarCross Ref
- [26] . 2021. UCSD-Adobe at MEDIQA 2021: Transfer learning and answer sentence selection for medical summarization. In Proceedings of the 20th Workshop on Biomedical Language Processing (BioNLP’21). 257–262.Google ScholarCross Ref
- [27] . 2021. Contrastive learning for many-to-many multilingual neural machine translation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP’21). 244–258.Google ScholarCross Ref
- [28] . 2020. Stanza: A Python natural language processing toolkit for many human languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL’20). 101–108.Google ScholarCross Ref
- [29] . 2020. ProphetNet: Predicting future N-gram for sequence-to-sequence pre-training. In Findings of the Association for Computational Linguistics: EMNLP 2020. 2401–2410.Google ScholarCross Ref
- [30] . 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research 21, 1 (2020), 5485–5551.Google ScholarDigital Library
- [31] . 2021. WBI at MEDIQA 2021: Summarizing consumer health questions with generative transformers. In Proceedings of the 20th Workshop on Biomedical Language Processing (BioNLP’21). 86–95.Google ScholarCross Ref
- [32] . 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL’17). 1073–1083.Google ScholarCross Ref
- [33] . 2022. A sentence is worth 128 pseudo tokens: A semantic-aware contrastive learning framework for sentence embeddings. In Findings of the Association for Computational Linguistics: ACL 2022. 246–256.Google ScholarCross Ref
- [34] . 2021. Se4exsum: An integrated semantic-aware neural approach with graph convolutional network for extractive text summarization. Transactions on Asian and Low-Resource Language Information Processing 20, 6 (2021), 1–22.Google ScholarDigital Library
- [35] . 2021. CLINE: Contrastive learning with semantic negative examples for natural language understanding. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP’21). 2332–2342.Google ScholarCross Ref
- [36] . 2022. Sequence level contrastive learning for text summarization. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI’22). 11556–11565.Google ScholarCross Ref
- [37] . 2021. Reinforcement learning for abstractive question summarization with question-aware semantic rewards. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP’21). 249–255.Google ScholarCross Ref
- [38] . 2022. CHQ-Summ: A dataset for consumer healthcare question summarization. arXiv:2206.06581. Retrieved from https://arxiv.org/abs/2206.06581Google Scholar
- [39] . 2021. NLM at MEDIQA 2021: Transfer learning-based approaches for consumer question and multi-answer summarization. In Proceedings of the 20th Workshop on Biomedical Language Processing (BioNLP’21). 291–301.Google ScholarCross Ref
- [40] . 2021. xMoCo: Cross momentum contrastive learning for open-domain question answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP’21). 6120–6129.Google ScholarCross Ref
- [41] . 2019. Reducing word omission errors in neural machine translation: A contrastive learning approach. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL’19). 6191–6196.Google ScholarCross Ref
- [42] . 2022. Label refinement via contrastive learning for distantly-supervised named entity recognition. In Findings of the Association for Computational Linguistics: NAACL 2022. 2656–2666.Google ScholarCross Ref
- [43] Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, and Pengtao Xie. 2020. MedDialog: Large-scale medical dialogue datasets. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP’20). 9241–9250.Google Scholar
- [44] . 2020. PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization. In Proceedings of the 37th International Conference on Machine Learning (ICML’20). 11328–11339.Google Scholar
- [45] . 2022. Focus-driven contrastive learning for medical question summarization. In Proceedings of the 29th International Conference on Computational Linguistics (COLING’22). 6176–6186.Google Scholar
- [46] . 2022. MCSE: Multimodal contrastive learning of sentence embeddings. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL’22). 5959–5969.Google ScholarCross Ref
- [47] . 2023. Ga-scs: Graph-augmented source code summarization. ACM Transactions on Asian and Low-Resource Language Information Processing 22, 2 (2023), 1–19.Google ScholarDigital Library
- [48] . 2022. Frequency-aware contrastive learning for neural machine translation. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI’22). 11712–11720.Google ScholarCross Ref
- [49] . 2020. Chinese medical question answer selection via hybrid models based on CNN and GRU. Multimedia Tools and Applications 79, 21-22 (2020), 14751–14776.Google Scholar
- [50] . 2023. From softmax to nucleusmax: A novel sparse language model for Chinese radiology report summarization. ACM Transactions on Asian and Low-Resource Language Information Processing 22, 6 (2023), 1–21.Google ScholarDigital Library
Index Terms
- Medical Question Summarization with Entity-driven Contrastive Learning
Recommendations
ProCo: Prototype-Aware Contrastive Learning for Long-Tailed Medical Image Classification
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022AbstractMedical image classification has been widely adopted in medical image analysis. However, due to the difficulty of collecting and labeling data in the medical area, medical image datasets are usually highly-imbalanced. To address this problem, ...
Enhancing Aspect-Based Sentiment Analysis with Supervised Contrastive Learning
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementMost existing aspect-based sentiment analysis (ABSA) research efforts are devoted to extracting the aspect-dependent sentiment features from the sentence towards the given aspect. However, it is observed that about 60% of the testing aspects in commonly ...
A Semantic Similarity Distance-Aware Contrastive Learning for Abstractive Summarization
PRICAI 2023: Trends in Artificial IntelligenceAbstractRecently, contrastive learning has been extended from visual representation to summarization tasks. Abstractive summarization aims to generate a short description for a document while retaining significant information. At present, the methods of ...
Comments