Abstract Automatic Text Summarization (ATS) is becoming much more important because of
the huge amount of textual content that grows exponentially on the Internet and the various …

Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

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 …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Transformers-based models, such as BERT, have been one of the most successful deep
learning models for NLP. Unfortunately, one of their core limitations is the quadratic …

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 …

Pre-trained language models (eg, BERT (Devlin et al., 2018) and its variants) have achieved
remarkable success in varieties of NLP tasks. However, these models usually consist of …

Modern summarization models generate highly fluent but often factually unreliable outputs.
This motivated a surge of metrics attempting to measure the factuality of automatically …

Bidirectional Encoder Representations from Transformers (BERT) represents the latest
incarnation of pretrained language models which have recently advanced a wide range of …