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 …

Dialogue summarization aims to condense the original dialogue into a shorter version
covering salient information, which is a crucial way to reduce dialogue data overload …

The propensity of abstractive summarization models to make factual errors has been studied
extensively, including design of metrics to detect factual errors and annotation of errors in …

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

Text summarization is a subtask of natural language processing referring to the automatic
creation of a concise and fluent summary that captures the main ideas and topics from one …

Annotating task-oriented dialogues is notorious for the expensive and difficult data collection
process. Few-shot dialogue state tracking (DST) is a realistic solution to this problem. In this …

Dialogue, the most fundamental and specially privileged arena of language, gains
increasing ubiquity across the Web in recent years. Quickly going through the long dialogue …

Abstractive dialogue summarization generates a concise and fluent summary covering the
salient information in a dialogue among two or more interlocutors. It has attracted significant …

Existing works on long-term open-domain dialogues focus on evaluating model responses
within contexts spanning no more than five chat sessions. Despite advancements in long …

Compared to news and chat summarization, the development of meeting summarization is
hugely decelerated by the limited data. To this end, we introduce a versatile Chinese …