The recent success of zero-and few-shot prompting with models like GPT-3 has led to a
paradigm shift in NLP research. In this paper, we study its impact on text summarization …

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 …

Recent advances in large language models (LLMs) have demonstrated remarkable
successes in zero-and few-shot performance on various downstream tasks, paving the way …

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 …

Abstractive dialogue summarization generates a concise and fluent summary covering the
salient information in a dialogue among two or more interlocutors. It has attracted significant …

Automatic summarization of legal case judgements, which are known to be long and
complex, has traditionally been tried via extractive summarization models. In recent years …

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 …

Large language models (LLMs) have achieved dramatic proficiency over NLP tasks with
normal length. Recently, multiple studies have committed to extending the context length …

A system that could reliably identify and sum up the most important points of a conversation
would be valuable in a wide variety of real-world contexts, from business meetings to …

As the number of recorded meetings increases, it becomes increasingly important to utilize
summarization technology to create useful summaries of these recordings. However, there is …