December 8: Michael Paul: Summarizing Contrastive Viewpoints in Opinionated Text
AVW 2120
Performing multi-document summarization of opinionated text has unique
challenges because it is important to recognize that the same
information may be presented in different ways from different
viewpoints. In this talk, we will present a special kind of
contrastive summarization approach intended to highlight this
phenomenon and to help users digest conflicting opinions. To do this,
we introduce a new graph-based algorithm, Comparative LexRank, to
score sentences in a summary based on a combination of both
representativeness of the collection and comparability between
opposing viewpoints. We then address the issue of how to automatically
discover and extract viewpoints from unlabeled text, and we experiment
with a novel two-dimensional topic model for the task of unsupervised
clustering of documents by viewpoint. Finally, we discuss how these
two stages can be combined to both automatically extract and summarize
viewpoints in an interesting way. Results are presented on two
political opinion data sets.
This project was joint work with ChengXiang Zhai and Roxana Girju.
Bio: Michael Paul is a first-year Ph.D. student of Computer Science at
the Johns Hopkins University and a member of the Center for Language
and Speech Processing. He earned a B.S. from the University of
Illinois at Urbana-Champaign in 2009. He is currently a Graduate
Research Fellow of the National Science Foundation and a Dean's Fellow
of the Whiting School of Engineering.
