Abstract
Multi-sentence compression (MSC) aims to
generate a grammatical but reduced compression from multiple input sentences while retaining their key information. Previous dominating approach for MSC is the extractionbased word graph approach. A few variants further leveraged lexical substitution to
yield more abstractive compression. However, two limitations exist. First, the word
graph approach that simply concatenates fragments from multiple sentences may yield non-
fluent or ungrammatical compression. Second, lexical substitution is often inappropriate without the consideration of context information. To tackle the above-mentioned issues, we present a neural rewriter for multisentence compression that does not need any
parallel corpus. Empirical studies have shown
that our approach achieves comparable results
upon automatic evaluation and improves the
grammaticality of compression based on human evaluation. A parallel corpus with more
than 140,000 (sentence group, compression)
pairs is also constructed as a by-product for future research