In this paper we present a joint content selection and compression model for single-document summarization. The model operates over a phrase-based representation of the source doc...
We present a fast query-based multi-document summarizer called FastSum based solely on word-frequency features of clusters, documents and topics. Summary sentences are ranked by a...
We present a new approach to automatic summarization based on neural nets, called NetSum. We extract a set of features from each sentence that helps identify its importance in the...
Krysta Marie Svore, Lucy Vanderwende, Christopher ...
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that th...
tion--the art of abstracting key content from one or more information sources--has become an integral part of everyday life. People keep abreast of world affairs by listening to ne...