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, queryfocused 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  .
The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available. We propose to decompose QFS into (1) query modeling (i. e., finding supportive evidence within a set of documents for a query) and (2) conditional language modeling (i.e., summary generation). We introduce MaRGE , a Masked ROUGE Regression framework for evidence estimation and ranking which relies on a unified representation for summaries and queries, so that summaries in generic data can be converted into proxy queries for learning a query model. Experiments across QFS benchmarks and query types show that our model achieves state-of-the-art performance despite learning from weak supervision .
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sequence-to-sequence models to generate generic summaries , i.
models that create generic summaries from single or multiple documents. In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available. We propose to decompose QFS into (1) query modeling (i.
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summaries which do not correspond to any pre-specified queries.However, due to the lack of training data, queryfocused 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
finding supportive evidence within a set of documents for a query) and (2) conditional language modeling (i.e., summary generation). We introduce MaRGE
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composed of a novel
for evidence estimation and ranking which relies on a
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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
so that summaries in generic data can be converted into proxy queries for learning a query model. Experiments across QFS benchmarks
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show that our model achieves state-of-the-art performance despite learning from weak supervision