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 …

Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP)
algorithms, aims to create concise and accurate summaries, thereby significantly reducing …

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

Current end-to-end neural conversation models inherently lack the flexibility to impose
semantic control in the response generation process, often resulting in uninteresting …

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

Evaluation of a document summarization system has been a critical factor to impact the
success of the summarization task. Previous approaches, such as ROUGE, mainly consider …

Visual dialogue is a challenging task that needs to extract implicit information from both
visual (image) and textual (dialogue history) contexts. Classical approaches pay more …

In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that
comprises four elements: faithfulness (degree of factual consistency with the source), focus …

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 …

In recent years, reference-based and supervised summarization evaluation metrics have
been widely explored. However, collecting human-annotated references and ratings are …