NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd

Integrating cohesion and coherence for automatic summarization

This paper presents the integration of cohesive properties of text with coherence relations, to obtain an adequate representation of text for automatic summarization. A summarizer based on Lexical Chains is enchanced with rhetorical and argumentative structure obtained via Discourse Markers. When evaluated with newspaper corpus, this integration yields only slight improvement in the resulting summaries and cannot beat a dummy baseline consisting of the first sentence in the document. Nevertheless, we argue that this approach relies on basic linguistic mechanisms and is therefore genreindependent.