This paper is in the following e-collection/theme issue:

Theme Issue 2022: Health Natural Language Processing and Applications (CHIP) (27) Clinical Informatics (1309) New Methods (1128) Natural Language Processing (900) Clinical Information and Decision Making (1894)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38052, first published .
Exploiting Intersentence Information for Better Question-Driven Abstractive Summarization: Algorithm Development and Validation

Exploiting Intersentence Information for Better Question-Driven Abstractive Summarization: Algorithm Development and Validation

Exploiting Intersentence Information for Better Question-Driven Abstractive Summarization: Algorithm Development and Validation

Authors of this article:

Xin Wang1 Author Orcid Image ;   Jian Wang1 Author Orcid Image ;   Bo Xu1 Author Orcid Image ;   Hongfei Lin1 Author Orcid Image ;   Bo Zhang1 Author Orcid Image ;   Zhihao Yang1 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Pawar D, Phansalkar S. MindWellQA: A Semantically Enriched Evidence-Based QA System for Psychological Disorders. IEEE Access 2025;13:190003 View

Conference Proceedings

  1. Moung E, Tong T, Sufian M, Liaw V, Farzamnia A, Yahya F. 2024 14th International Conference on Computer and Knowledge Engineering (ICCKE). Optimizing Question-Answering Framework Through Integration of Text Summarization Model and Third-Generation Generative Pre-Trained Transformer View