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 …

The scarcity of comprehensive up-to-date studies on evaluation metrics for text
summarization and the lack of consensus regarding evaluation protocols continue to inhibit …

Text summarization aims at compressing long documents into a shorter form that conveys
the most important parts of the original document. Despite increased interest in the …

We study unsupervised multi-document summarization evaluation metrics, which require
neither human-written reference summaries nor human annotations (eg preferences …

We provide a literature review about Automatic Text Summarization (ATS) systems. We
consider a citation-based approach. We start with some popular and well-known papers that …

Extractive summarization for academic articles in natural sciences and medicine has
attracted attention for a long time. However, most existing extractive summarization models …

With the development of pre-trained language models and large-scale datasets, automatic
text summarization has attracted much attention from the community of natural language …

Automatic evaluation metrics are indispensable for evaluating generated text. To date, these
metrics have focused almost exclusively on the content selection aspect of the system …

Abstractive summarization has made tremendous progress in recent years. In this work, we
perform fine-grained human annotations to evaluate long document abstractive …

Measuring the performance of natural language processing models is challenging.
Traditionally used metrics, such as BLEU and ROUGE, originally devised for machine …