Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

Despite the widespread use of artificial intelligence (AI), designing user experiences (UX) for
AI-powered systems remains challenging. UX designers face hurdles understanding AI …

Dialogue is an essential part of human communication and cooperation. Existing research
mainly focuses on short dialogue scenarios in a one-on-one fashion. However, multi-person …

Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP)
algorithms, aims to create concise and accurate summaries, thereby significantly reducing …

Cross-lingual summarization is the task of generating a summary in one language (eg,
English) for the given document (s) in a different language (eg, Chinese). Under the …

Factual inconsistencies in generated summaries severely limit the practical applications of
abstractive dialogue summarization. Although significant progress has been achieved by …

We present ClidSum, a benchmark dataset for building cross-lingual summarization systems
on dialogue documents. It consists of 67k+ dialogue documents from two subsets (ie …

Summarizing conversations via neural approaches has been gaining research traction
lately, yet it is still challenging to obtain practical solutions. Examples of such challenges …

A system that could reliably identify and sum up the most important points of a conversation
would be valuable in a wide variety of real-world contexts, from business meetings to …

Multi-party conversations (MPCs) are a more practical and challenging scenario involving
more than two interlocutors. This research topic has drawn significant attention from both …