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 …

A new metric\texttt {BaryScore} to evaluate text generation based on deep contextualized
embeddings eg, BERT, Roberta, ELMo) is introduced. This metric is motivated by a new …

Assessing the quality of natural language generation (NLG) systems through human
annotation is very expensive. Additionally, human annotation campaigns are time …

Automatic text summarization aims to cut down readers' time and cognitive effort by reducing
the content of a text document without compromising on its essence. Ergo, informativeness …

The design of a metric between probability distributions is a longstanding problem motivated
by numerous applications in Machine Learning. Focusing on continuous probability …

Text summarization tools can help biomedical researchers and clinicians reduce the time
and effort needed for acquiring important information from numerous documents. It has been …

State-of-the-art summarization systems are trained and evaluated on massive datasets
scraped from the web. Despite their prevalence, we know very little about the underlying …

Training regimes based on Maximum Likelihood Estimation (MLE) suffer from known
limitations, often leading to poorly generated text sequences that lack of coherence …

With the growth of fake news and disinformation, the NLP community has been working to
assist humans in fact-checking. However, most academic research has focused on model …

With the rapid and unprecedented growth of textual data in recent years, there is a
remarkable need for automatic text summarization models to retrieve useful information from …