Towards Automated Related Work Summarization

Abstract

This thesis introduces and describes the novel problem of automated related work summarization. Given multiple articles (e.g., conference or journal papers) as input, and a set of keywords that describes a target paper?s topics of interest in a hierarchical fashion, a related work summarization system creates a topic-biased summary of related work specific to the target paper. This thesis has two main contributions. First, I conducted a deep manual analysis on various aspects of related work sections to identify their important characteristics in locating appropriate information for summarization and generation processes. Second, based on the observations from my manual analysis, I have developed my initial prototype Related Work Summarization system, namely ReWoS, which creates its extractive summaries using two different strategies for locating appropriate sentences for general topics as well as detailed ones. The proposed ReWoS system significantly outperforms baseline systems in terms of human evaluation measures designed specific to the task.