Multi-document summarization (MDS) is an effective tool for information aggregation that
generates an informative and concise summary from a cluster of topic-related documents …

Transformer architectures are highly expressive because they use self-attention
mechanisms to encode long-range dependencies in the input sequences. In this paper, we …

Since the proposal of transformers, these models have been limited to bounded input
lengths, because of their need to attend to every token in the input. In this work, we propose …

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) …

Transformer-based models have achieved state-of-the-art performance on short-input
summarization. However, they still struggle with summarizing longer text. In this paper, we …

Fine-tuning pretrained models for automatically summarizing doctor-patient conversation
transcripts presents many challenges: limited training data, significant domain shift, long and …

Recently, neural topic models (NTMs) have been incorporated into pre-trained language
models (PLMs), to capture the global semantic information for text summarization. However …

Specifically focusing on the landscape of abstractive text summarization, as opposed to
extractive techniques, this survey presents a comprehensive overview, delving into state-of …

Text summarization (TS) is an important NLP task. Pre-trained Language Models (PLMs)
have been used to improve the performance of TS. However, PLMs are limited by their need …