Nonfactoid question answering (QA) is one of the most extensive yet challenging
applications and research areas in natural language processing (NLP). Existing methods fall …

Question-driven summarization has been recently studied as an effective approach to
summarizing the source document to produce concise but informative answers for non …

Text summarization on non-factoid question answering (NQA) aims at identifying the core
information of redundant answer guidance using questions, which can dramatically improve …

We formulate a document summarization method to extract passage-level answers for non-
factoid queries, referred to as answer-biased summaries. We propose to use external …

Question Generation (QG) is the task of generating a plausible question for a given<
passage, answer> pair. Template-based QG uses linguistically-informed heuristics to …

We aim at summarizing answers in community question-answering (CQA). While most
previous work focuses on factoid question-answering, we focus on the non-factoid question …

Community question answering (CQA) is an important but challenging task. Meantime, as
the theory of deep learning develops, remarkable progress has been made by deep neural …

This paper describes a novel hierarchical attention network for reading comprehension style
question answering, which aims to answer questions for a given narrative paragraph. In the …

Online search engines are a popular source of medical information for users, where users
can enter questions and obtain relevant answers. It is desirable to generate answer …

We present a novel answer summarization method for community Question Answering
services (cQAs) to address the problem of “incomplete answer”, ie, the “best answer” of a …