Question-aware transformer models for consumer health question summarization

Yadav, Shweta; Gupta, Deepak; Abacha, Asma Ben; Demner‐Fushman, Dina

doi:10.1016/j.jbi.2022.104040

Cited by 18 publications

(13 citation statements)

References 28 publications

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“…Sanchez-Gomez et al (2022) developed a query-focused multi-objective memetic algorithm to generate extractive summaries for medical texts. Yadav et al (2022) designed a consumer health question summarization approach by introducing various cloze tasks to pre-trained transformer models, aiming for better coverage of the question focus in the summarized questions. Additionally, Khatter and Ahlawat (2022) proposed a hybrid model that integrates self-attention into the bi-directional LSTM auto-encoder to generate information-rich abstracts through extractive summarization.…”

Section: Related Workmentioning

confidence: 99%

“…Recent advancements in this process have been reported, including single-document (Mao et al , 2019), multi-document (Agarwal and Chatterjee, 2022), query-focused (Sanchez-Gomez et al , 2022) text summarization and so on. Specifically, prior studies have attempted to apply text summarization techniques to the health-care domain, such as electronic health records (Pivovarov and Elhadad, 2015) and question answering (Yadav et al , 2022). Although these studies were designed to help health care professionals or consumers with health-related document retrieval, extraction or classification, they did not consider how to assist lay people (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Advanced intelligent health advice with informative summaries to facilitate treatment decision-making

Liu

Chen

2023

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Purpose Whether automatically generated summaries of health social media can assist users in appropriately managing their diseases and ensuring better communication with health professionals becomes an important issue. This paper aims to develop a novel deep learning-based summarization approach for obtaining the most informative summaries from online patient reviews accurately and effectively. Design/methodology/approach This paper proposes a framework to generate summaries that integrates a domain-specific pre-trained embedding model and a deep neural extractive summary approach by considering content features, text sentiment, review influence and readability features. Representative health-related summaries were identified, and user judgements were analysed. Findings Experimental results on the three real-world health forum data sets indicate that awarding sentences without incorporating all the adopted features leads to declining summarization performance. The proposed summarizer significantly outperformed the comparison baseline. User judgement through the questionnaire provides realistic and concrete evidence of crucial features that remarkably influence patient forum review summaries. Originality/value This study contributes to health analytics and management literature by exploring users’ expressions and opinions through the health deep learning summarization model. The research also developed an innovative mindset to design summarization weighting methods from user-created content on health topics.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced intelligent health advice with informative summaries to facilitate treatment decision-making

Liu

Chen

2023

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“…Transformers Transformer is a deep learning architecture proposed by Google in 2017. It has achieved great success in the field of NLP [44][45][46]. Due to the unique attention mechanism and the excellent performance in the field of NLP, researchers have great interest in its application in trajectory prediction.…”

Section: Related Workmentioning

confidence: 99%

Multi-granularity scenarios understanding network for trajectory prediction

Yang

et al. 2022

Complex Intell. Syst.

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Understanding agents’ motion behaviors under complex scenes is crucial for intelligent autonomous moving systems (like delivery robots and self-driving cars). It is challenging duo to the inherent uncertain of future trajectories and the large variation in the scene layout. However, most recent approaches ignored or underutilized the scenario information. In this work, a Multi-Granularity Scenarios Understanding framework, MGSU, is proposed to explore the scene layout from different granularity. MGSU can be divided into three modules: (1) A coarse-grained fusion module uses the cross-attention to fuse the observed trajectory with the semantic information of the scene. (2) The inverse reinforcement learning module generates optimal path strategy through grid-based policy sampling and outputs multiple scene paths. (3) The fine-grained fusion module integrates the observed trajectory with the scene paths to generate multiple future trajectories. To fully explore the scene information and improve the efficiency, we present a novel scene-fusion Transformer, whose encoder is used to extract scene features and the decoder is used to fuse scene and trajectory features to generate future trajectories. Compared with the current state-of-the-art methods, our method decreases the ADE errors by 4.3% and 3.3% by gradually integrating different granularity of scene information on SDD and NuScenes, respectively. The visualized trajectories demonstrate that our method can accurately predict future trajectories after fusing scene information.

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“…In recent years the pre-trained models have shown notable performance on many downstream Natural Language Processing (NLP) tasks such as Question-Answering(QA), summarization, machine translation, sentiment analysis, etc. [1], [2], [3], [4], [5], [6]. To use the pre-train models for the task other than the one on which it has been trained [7], fine-tuning on the task-specific supervised dataset is required.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Low-Resource Question-Answering Performance Through Word Seeding and Customized Refinement

Pandya,

Bhatt

2024

IJACSA

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The state-of-the-art approaches in Question-Answering (QA) systems necessitate extensive supervised training datasets. In low-resource languages (LRL), the scarcity of data poses a bottleneck, and the manual annotation of labeled data is a rigorous process. Addressing this challenge, some recent efforts have explored cross-lingual or multilingual QA learning by leveraging training data from resource-rich languages (RRL). However, the efficiency of such approaches relies on syntactic compatibility between languages. The paper introduces the innovative method that involves seeding LRL data into RRL to create a bilingual supervised corpus while preserving the syntactical structure of RRL. The method employs the translation and transliteration of selected parts-of-speech (POS) category words. Additionally, the paper also proposes a customized approach to fine-tune the models using bilingual data. Employing the bilingual data and the proposed fine-tuning approach, the most successful model has achieved a 75.62 F1 score on the XQuAD Hindi dataset and a 68.92 F1 score on the MLQA Hindi dataset in a zero-shot architecture. In the experiments conducted using few-shot learning setup, the highest F1 scores of 79.17 on the XQuAD Hindi dataset and 70.42 on the MLQA Hindi dataset have been achieved.

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Question-aware transformer models for consumer health question summarization

Cited by 18 publications

References 28 publications

Advanced intelligent health advice with informative summaries to facilitate treatment decision-making

Advanced intelligent health advice with informative summaries to facilitate treatment decision-making

Multi-granularity scenarios understanding network for trajectory prediction

Enhancing Low-Resource Question-Answering Performance Through Word Seeding and Customized Refinement

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