Question Answering Summarization of Multiple Biomedical Documents

Autor:	Zhongmin Shi, Fred Popowich, Baohua Gu, Gabor Melli, Yudong Liu, Yang Wang, Mehdi M. Kashani, Anoop Sarkar
Rok vydání:	2007
Předmět:	Information retrieval Computer science business.industry Process (engineering) MEDLINE Information needs computer.software_genre Automatic summarization Semantic role labeling Question answering Selection (linguistics) Artificial intelligence business computer Sentence Natural language processing
Zdroj:	Advances in Artificial Intelligence ISBN: 9783540726647 Canadian Conference on AI
DOI:	10.1007/978-3-540-72665-4_25
Popis:	In this paper we introduce a system that automatically summarizes multiple biomedical documents relevant to a question. The system extracts biomedical and general concepts by utilizing concept-level knowledge from domain-specific and domain-independent sources. Semantic role labeling, semantic subgraph-based sentence selection and automatic post-editing are involved in the process of finding the information need. Due to the absence of expert-written summaries of biomedical documents, we propose an approximate evaluation by taking MEDLINE abstracts as expert-written summaries. Evaluation results indicate that our system does help in answering questions and the automatically generated summaries are comparable to abstracts of biomedical articles, as evaluated using the ROUGE measure.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b244781ab02dcfaac7849e4b7de18fc1 https://doi.org/10.1007/978-3-540-72665-4_25 Zobrazit plný text záznamu