Title: Automatically Generating Gene Summaries from Biomedical Literature (X. LING, J. JIANG, X. He, C.~X. ZHAI, Q.~Z. MEI, B. SCHATZ)

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Abstract: Finding information about a target gene from biomedical literature is a very common task that all biologists routinely perform. By using a literature search engine, in particular, PubMed, a biologist needs to spend considerable efforts reading the retrieved articles in order to locate the most relevant knowledge about the gene. In this work, we study how to automatically generate a summary from the literature for a target gene to make it easier for biologists to access the already-discovered knowledge. We present a two-stage summarization method, which involves first retrieving relevant articles and then extracting the most informative sentences from the retrieved articles to generate a structured gene summary. The generated summary explicitly covers multiple aspects of a gene, such as the sequence information, mutant phenotypes, and molecular interaction with other genes. We conducted experiments on Drosophila genes from FlyBase and a subset of Medline abstracts about Drosophila. The generated summaries are quite informative, indicating that our approaches are effective in automatically summarizing literature information about genes.

Biography: Xu Ling is a first-year Ph.D. student in the Department of Computer Science at the University of Illinois. Her research interests are mainly in bioinformatics, including gene regulation, biomedical literature mining, and genome sequence analysis.