Multi-document summarization (MDS) is an effective tool for information aggregation that
generates an informative and concise summary from a cluster of topic-related documents …

Summarization, is to reduce the size of the document while preserving the meaning, is one
of the most researched areas among the Natural Language Processing (NLP) community …

Meetings are a key component of human collaboration. As increasing numbers of meetings
are recorded and transcribed, meeting summaries have become essential to remind those …

An abundance of datasets and availability of reliable evaluation metrics have resulted in
strong progress in factoid question answering (QA). This progress, however, does not easily …

In the past few years, neural abstractive text summarization with sequence-to-sequence
(seq2seq) models have gained a lot of popularity. Many interesting techniques have been …

The core of information retrieval (IR) is to identify relevant information from large-scale
resources and return it as a ranked list to respond to user's information need. In recent years …

With the rapid development and popularization of Internet and mobile communication
technologies, text data mining has attracted much attention. In particular, with the wide use …

Question generation is an important yet challenging problem in Artificial Intelligence (AI),
which aims to generate natural and relevant questions from various input formats, eg …

The focus of automatic text summarization research has exhibited a gradual shift from
extractive methods to abstractive methods in recent years, owing in part to advances in …

Word embeddings that consider context have attracted great attention for various natural
language processing tasks in recent years. In this paper, we utilize contextualized word …