Before revision:  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  .
After revision:  We propose Marge , a Masked ROUGE Regression framework composed of a novel 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 .