Topical Coherence for Graph-based Extractive Summarization

We present an approach for extractive single-document summarization. Our ap-proach is based on a weighted graphical representation of documents obtained by topic modeling. We optimize importance, coherence and non-redundancy simulta-neously using ILP. We compare ROUGE scores of our system with state-of-the-art results on scientific articles from PLOS Medicine and on DUC 2002 data. Hu-man judges evaluate the coherence of sum-maries generated by our system in com-parision to two baselines. Our approach obtains competitive performance.