Modeling long texts has been an essential technique in the field of natural language
processing (NLP). With the ever-growing number of long documents, it is important to …

Text summarization helps readers capture salient information from documents, news,
interviews, and meetings. However, most state-of-the-art pretrained language models (LM) …

Long document summarization poses obstacles to current generative transformer-based
models because of the broad context to process and understand. Indeed, detecting long …

Progress in summarizing long texts is inhibited by the lack of appropriate evaluation
frameworks. When a long summary must be produced to appropriately cover the facets of …

Generative transformer-based models have reached cutting-edge performance in long
document summarization. Nevertheless, this task is witnessing a paradigm shift in …

Text segmentation is important for signaling a document's structure. Without segmenting a
long document into topically coherent sections, it is difficult for readers to comprehend the …

Abstractive summarization has made tremendous progress in recent years. In this work, we
perform fine-grained human annotations to evaluate long document abstractive …

Neural attention models have achieved significant improvements on many natural language
processing tasks. However, the quadratic memory complexity of the self-attention module …

We present an empirical study of adapting an existing pretrained text-to-text model for long-
sequence inputs. Through a comprehensive study along three axes of the pretraining …

Abstractive text summarization is a task in natural language processing that automatically
generates the summary from the source document in a human-written form with minimal loss …