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.
More recently, Mehdad proposed a supervised approach for meeting summarization, in which they generate an entailment graph of sentences. The nodes in the graph are the linked sentences and edges are the entailment relations between nodes; such relations help to identify non-redundant and informative sentences. Their fusion approach used MSC @cite_0 , which generates an informative sentence by combining several sentences in a word-graph structure. However, Filippova's method produces low linguistic quality as the ranking of generated sentences is based on edge weights calculated only using word collocations. By contrast, our method selects sentences by jointly maximizing informativeness and readability and generates informative, well-formed and readable summaries.
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We consider the task of summarizing a cluster of related sentences with a short sentence which we call multi-sentence compression and present a simple approach based on shortest paths in word graphs. The advantage and the novelty of the proposed method is that it is syntaxlean and requires little more than a tokenizer and a tagger. Despite its simplicity, it is capable of generating grammatical and informative summaries as our experiments with English and Spanish data demonstrate.