Medical Question Summarization with Entity-driven Contrastive Learning

• URL: http://arxiv.org/abs/2304.07437v1
• Date: Sat, 15 Apr 2023 00:19:03 GMT
• Title: Medical Question Summarization with Entity-driven Contrastive Learning
• Authors: Sibo Wei, Wenpeng Lu, Xueping Peng, Shoujin Wang, Yi-Fei Wang and Weiyu Zhang
• Abstract summary: This paper proposes a novel medical question summarization framework using entity-driven contrastive learning (ECL) ECL employs medical entities in frequently asked questions (FAQs) as focuses and devises an effective mechanism to generate hard negative samples. We find that some MQA datasets suffer from serious data leakage problems, such as the iCliniq dataset's 33% duplicate rate.
• Score: 12.008269098530386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By summarizing longer consumer health questions into shorter and essential ones, medical question answering (MQA) 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 existing works have attempted to utilize Seq2Seq, reinforcement learning, or contrastive learning to solve the problem, 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 using entity-driven contrastive learning (ECL). ECL employs medical entities in frequently asked questions (FAQs) as focuses and devises an effective mechanism to generate hard negative samples. This approach forces models to pay attention to the crucial focus information and generate more ideal question summarization. Additionally, we find that some MQA datasets suffer from serious data leakage problems, such as the iCliniq dataset's 33% duplicate rate. To evaluate the related methods fairly, this paper carefully checks leaked samples to reorganize more reasonable datasets. Extensive experiments demonstrate that our ECL method outperforms state-of-the-art methods by accurately capturing question focus and generating medical question summaries. The code and datasets are available at https://github.com/yrbobo/MQS-ECL.

Related papers

• FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection [83.54960238236548]
  FEDMEKI not only preserves data privacy but also enhances the capability of medical foundation models. FEDMEKI allows medical foundation models to learn from a broader spectrum of medical knowledge without direct data exposure.
  arXiv  Detail & Related papers  (2024-08-17T15:18:56Z)
• RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions [3.182594503527438]
  We present RealMedQA, a dataset of realistic clinical questions generated by humans and an LLM. We show that the LLM is more cost-efficient for generating "ideal" QA pairs.
  arXiv  Detail & Related papers  (2024-08-16T09:32:43Z)
• ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering [54.80411755871931]
  Question Answering (QA) effectively evaluates language models' reasoning and knowledge depth. Chemical QA plays a crucial role in both education and research by effectively translating complex chemical information into readily understandable format. This dataset reflects typical real-world challenges, including an imbalanced data distribution and a substantial amount of unlabeled data that can be potentially useful. We introduce a QAMatch model, specifically designed to effectively answer chemical questions by fully leveraging our collected data.
  arXiv  Detail & Related papers  (2024-07-24T01:46:55Z)
• Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation [0.0]
  We propose a method that mitigates shortcomings by combining semantic search, retrieval augmented generation and question-answering. Our approach is quite efficient; requires minimal to no training; does not suffer from the 'hallucination' problem of LLMs. It ensures diversity, since the summary will not have repeated content but diverse answers to specific questions.
  arXiv  Detail & Related papers  (2024-01-03T00:09:34Z)
• SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization [50.01382938451978]
  We introduce a model-agnostic pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings indicate that our method not only bolsters performance but also effectively curbs variance for various language models.
  arXiv  Detail & Related papers  (2023-03-23T04:47:46Z)
• Focus-Driven Contrastive Learniang for Medical Question Summarization [18.33686557238865]
  We propose a novel question focus-driven contrastive learning framework (QFCL) On three medical benchmark datasets, our proposed model achieves new state-of-the-art results. Our QFCL model learns better sentence representations with the ability to distinguish different sentence meanings.
  arXiv  Detail & Related papers  (2022-09-01T14:15:46Z)
• Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching [58.26619456972598]
  Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. This paper studies a practical yet challenging FL problem, named textitFederated Semi-supervised Learning (FSSL) We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme.
  arXiv  Detail & Related papers  (2021-06-16T07:58:00Z)
• Question-aware Transformer Models for Consumer Health Question Summarization [20.342580435464072]
  We develop an abstractive question summarization model that leverages the semantic interpretation of a question via recognition of medical entities. When evaluated on the MeQSum benchmark corpus, our framework outperformed the state-of-the-art method by 10.2 ROUGE-L points.
  arXiv  Detail & Related papers  (2021-06-01T04:21:31Z)
• Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering [89.76059961309453]
  HeadQA dataset contains multiple-choice questions authorized for the public healthcare specialization exam. These questions are the most challenging for current QA systems. We present a Multi-step reasoning with Knowledge extraction framework (MurKe) We are striving to make full use of off-the-shelf pre-trained models.
  arXiv  Detail & Related papers  (2020-08-06T02:47:46Z)
• Question-Driven Summarization of Answers to Consumer Health Questions [17.732729654047983]
  We present the MEDIQA Answer Summarization dataset. This dataset is the first summarization collection containing question-driven summaries of answers to consumer health questions. We include results of baseline and state-of-the-art deep learning summarization models.
  arXiv  Detail & Related papers  (2020-05-18T20:36:11Z)
• DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction [67.91606509226132]
  Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. DeepEnroll is a cross-modal inference learning model to jointly encode enrollment criteria (tabular data) into a shared latent space for matching inference.
  arXiv  Detail & Related papers  (2020-01-22T17:51:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.