Recently, the emergence of pre-trained models (PTMs) has brought natural language
processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs …

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 …

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 …

Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural
Language Generation (NLG), ie, evaluating the generated text from multiple explainable …

Recent work pre-training Transformers with self-supervised objectives on large text corpora
has shown great success when fine-tuned on downstream NLP tasks including text …

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 …

As a crucial step in extractive document summarization, learning cross-sentence relations
has been explored by a plethora of approaches. An intuitive way is to put them in the graph …

Recently BERT has been adopted for document encoding in state-of-the-art text
summarization models. However, sentence-based extractive models often result in …

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 …