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 …

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 …

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 …

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

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

Generative transformer-based models have achieved state-of-the-art performance in text
summarization. Nevertheless, they still struggle in real-world scenarios with long documents …

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

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 …