Abstract Automatic Text Summarization (ATS) is an important area in Natural Language
Processing (NLP) with the goal of shortening a long text into a more compact version by …

Summarization, is to reduce the size of the document while preserving the meaning, is one
of the most researched areas among the Natural Language Processing (NLP) community …

Large language models (LLMs) have shown promise for automatic summarization but the
reasons behind their successes are poorly understood. By conducting a human evaluation …

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 …

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 …

It is well known that the standard likelihood training and approximate decoding objectives in
neural text generation models lead to less human-like responses for open-ended tasks such …

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 …

The paper surveys evaluation methods of natural language generation (NLG) systems that
have been developed in the last few years. We group NLG evaluation methods into three …

Currently used metrics for assessing summarization algorithms do not account for whether
summaries are factually consistent with source documents. We propose a weakly …

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 …