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ResQu: A Framework for Automatic Evaluation of Knowledge-Driven Automatic Summarization

Jaykumar, Nishita
http://orcid.org/0000-0001-8385-3356
http://rave.ohiolink.edu/etdc/view?acc_num=wright1464628801

Abstract Details

2016, Master of Science (MS), Wright State University, Computer Science.
Automatic generation of summaries that capture the salient aspects of a search resultset (i.e., automatic summarization) has become an important task in biomedical research. Automatic summarization offers an avenue for overcoming the information overload problem prevalent in large online digital libraries. However, across many of the knowledge-driven approaches for automatic summarization it is not always clear which features highly impact or influence the quality of a summary. Instead, there has been considerable focus on utilizing schema knowledge to facilitate browsing and exploration of generated summaries a posteriori. Informative features should not be ignored, since they could be utilized to help optimize the models that generate these semantic summaries in the first place. In this research, we adopt a leave-one-out approach to assess the impact of various features on the quality of automatically generated summaries that contain structured background knowledge. We first create the gold standard summaries, using information-theoretic methods, by extraction and validation, then the semantic summaries are transformed into an equivalent textual format. Finally, various similarity metrics, such as cosine similarity, euclidean distance, and jensen-shannon divergence are computed under different feature combinations, to assess summary quality against the textual gold standard. We report on the relative importance of the various features used to automatically generate the semantic summaries in a biomedical application. Our evaluation suggests that the proposed approach is an effective automatic evaluation method for assessing feature importance in automatically generated semantic summaries.
Amit Sheth, Ph.D. (Advisor)
Thomas Rindflesch, Ph.D. (Committee Member)
Delroy Cameron, Ph.D. (Committee Member)
Thirunarayan Krishnaprasad , Ph.D. (Committee Member)
Michael Raymer, Ph.D. (Committee Member)
72 p.
Bioinformatics; Biomedical Engineering; Biomedical Research; Computer Engineering; Computer Science; Science Education
Automatic Summarization, Summarization Evaluation, Abstractive Summaries, Quality-based evaluation, Semantic Similarity, Medical Subject Headings, MetaMap, SemRep

Recommended Citations

Citations

  • Jaykumar, N. (2016). ResQu: A Framework for Automatic Evaluation of Knowledge-Driven Automatic Summarization [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464628801

    APA Style (7th edition)

  • Jaykumar, Nishita. ResQu: A Framework for Automatic Evaluation of Knowledge-Driven Automatic Summarization. 2016. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1464628801.

    MLA Style (8th edition)

  • Jaykumar, Nishita. "ResQu: A Framework for Automatic Evaluation of Knowledge-Driven Automatic Summarization." Master's thesis, Wright State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464628801

    Chicago Manual of Style (17th edition)

wright1464628801
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© 2016, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.