Previous work on automatic summarization does not thoroughly consider coherence while generating the summary.We introduce a graph-based approach to summarize scientific articles.We employ coherence patterns to ensure that the generated summaries are coherent.The novelty of our model is twofold: we mine coherence patterns in a corpus of abstracts, and we propose a method to combine coherence, importance and non-redundancy to generate the summary.We optimize these factors simultaneously using Mixed Integer Programming.Our approach significantly outperforms baseline and state-of-the-art systems in terms of coherence (summary coherence assessment) and relevance (ROUGE scores).

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. 1