Before revision: 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.
After revision: The availability of large-scale datasets has driven the development of neural sequence-to-sequence models to generate generic summaries , 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 composed of a novel unified representation for summaries and queries, and a distantly supervised training task for answer evidence estimation.