The task of automatic document summarization aims at generating short summaries for
originally long documents. A good summary should cover the most important information of …

With the rapid growth of social media platforms, digitization of official records, and digital
publication of articles, books, magazines, and newspapers, lots of data are generated every …

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

We consider the problem of using sentence compression techniques to facilitate query-
focused multi-document summarization. We present a sentence-compression-based …

Emotion cause extraction is a challenging task for the fine-grained emotion analysis. Even
though a few studies have addressed the task using clause-level classification methods …

In the age of information overload, the ability to distill essential content from extensive texts
is invaluable. DeepExtract introduces an advanced framework for extractive summarization …

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

In this paper, we investigate the possibility to automatically generate sports news from live
text commentary scripts. As a preliminary study, we treat this task as a special kind of …

Twitter has become one of the most popular platforms for users to share information in real
time. However, as an individual tweet is short and lacks sufficient contextual information …

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 …