Abstractive Query Focused Summarization with Query-Free Resources
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The availability of large-scale datasets has driven the development of neural sequence-to-sequence models to generate generic summaries, i.e., summaries which do not correspond to any pre-specified queries. However, due to the lack of training data, query focused summarization (QFS) has been studied mainly with extractive methods. In this work, we consider the problem of leveraging only generic summarization resources to build an abstractive QFS system. We propose Marge, a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries, and a distantly supervised training task for answer evidence estimation. To further utilize generic data for generation, three attributes are incorporated during training and inference to control the shape of the final summary: evidence rank, query guidance, and summary length. Despite learning from minimal supervision, our system achieves state-of-the-art results in the distantly supervised setting across domains and query types.
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