Text summarization is one of the most challenging and interesting problems in NLP.
Although much attention has been paid to summarizing structured text like news reports or …

With the abundance of automatic meeting transcripts, meeting summarization is of great
interest to both participants and other parties. Traditional methods of summarizing meetings …

Automatically generating the “impression” section of a radiology report given the “findings”
section can summarize as much salient information of the “findings” section as possible, thus …

Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content
often comes from two or more interlocutors, exchanging information with each other. In such …

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

Recent studies in dialogue state tracking (DST) leverage historical information to determine
states which are generally represented as slot-value pairs. However, most of them have …

Multi-party multi-turn dialogue comprehension brings unprecedented challenges in handling
complicated scenarios, as the co-occurrence of multiple speakers causes complexity and …

Pre-trained models have brought remarkable success on the text summarization task. For
dialogue summarization, the subdomain of text summarization, utterances are concatenated …

Summaries generated from medical conversations can improve recall and understanding of
care plans for patients and reduce documentation burden for doctors. Recent advancements …

Quotations are crucial for successful explanations and persuasions in interpersonal
communications. However, finding what to quote in a conversation is challenging for …