  Abstractive summarization is an ideal form of summarization since it can
synthesize information from multiple documents to create concise informative
summaries. In this work, we aim at developing an abstractive summarizer. First,
our proposed approach identifies the most important document in the
multi-document set. The sentences in the most important document are aligned to
sentences in other documents to generate clusters of similar sentences. Second,
we generate K-shortest paths from the sentences in each cluster using a
word-graph structure. Finally, we select sentences from the set of shortest
paths generated from all the clusters employing a novel integer linear
programming (ILP) model with the objective of maximizing information content
and readability of the final summary. Our ILP model represents the shortest
paths as binary variables and considers the length of the path, information
score and linguistic quality score in the objective function. Experimental
results on the DUC 2004 and 2005 multi-document summarization datasets show
that our proposed approach outperforms all the baselines and state-of-the-art
extractive summarizers as measured by the ROUGE scores. Our method also
outperforms a recent abstractive summarization technique. In manual evaluation,
our approach also achieves promising results on informativeness and
readability.
