Extractive multi-document summarization is the task of choosing sentences from a set of documents to compose a summary text in response to a user query. We propose a generative ap...
A well-known challenge of information retrieval is how to infer a user's underlying information need when the input query consists of only a few keywords. Question Answering (...
This paper describes the design and evaluation of an extractive summarizer for educational science content called COGENT. COGENT extends MEAD based on strategies elicited from an ...
Sebastian de la Chica, Faisal Ahmad, James H. Mart...
Probabilistic models of sentence comprehension are increasingly relevant to questions concerning human language processing. However, such models are often limited to syntactic fac...
Although answering list questions is not a new research area, answering them automatically still remains a challenge. The median F-score of systems that participated in TREC 2007 ...