2007 QASummaOfMultBiomedDocs

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Subject Headings: Text Summarization Task, Biomedical Discipline.


Cited By



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.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2007 QASummaOfMultBiomedDocsZhongmin Shi
Gabor Melli
Yang Wang
Yudong Liu
Baohua Gu
Mehdi M. Kashani
Anoop Sarkar
Fred Popowich
Question Answering Summarization of Multiple Biomedical DocumentsProceedings of Canadian AI Conferencehttp://www.cs.sfu.ca/~anoop/papers/pdf/biosquash.pdf2007