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 …

A wide variety of NLP applications, such as machine translation, summarization, and dialog,
involve text generation. One major challenge for these applications is how to evaluate …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

This paper creates a paradigm shift with regard to the way we build neural extractive
summarization systems. Instead of following the commonly used framework of extracting …

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 …

Recent studies show that the predictive performance of graph neural networks (GNNs) is
inconsistent and varies across different experimental runs, even with identical parameters …

Automated evaluation metrics as a stand-in for manual evaluation are an essential part of
the development of text-generation tasks such as text summarization. However, while the …

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 …

Extractive summarization is a crucial task in natural language processing that aims to
condense long documents into shorter versions by directly extracting sentences. The recent …

Joint entity and relation extraction is an essential task in information extraction, which aims
to extract all relational triples from unstructured text. However, few existing works consider …