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 …

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 …

A desirable property of a reference-based evaluation metric that measures the content
quality of a summary is that it should estimate how much information that summary has in …

Different from general documents, it is recognised that the ease with which people can
understand a biomedical text is eminently varied, owing to the highly technical nature of …

Human evaluation for summarization tasks is reliable but brings in issues of reproducibility
and high costs. Automatic metrics are cheap and reproducible but sometimes poorly …

Reference-based metrics such as ROUGE or BERTScore evaluate the content quality of a
summary by comparing the summary to a reference. Ideally, this comparison should …

Large Language Models (LLMs) have enabled new ways to satisfy information needs.
Although great strides have been made in applying them to settings like document ranking …

We present SacreROUGE, an open-source library for using and developing summarization
evaluation metrics. SacreROUGE removes many obstacles that researchers face when …

One of the main challenges in the development of summarization tools is summarization
quality evaluation. On the one hand, the human assessment of summarization quality …