The emergence of large language models (LLMs) has marked a significant breakthrough in
natural language processing (NLP), fueling a paradigm shift in information acquisition …

Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP)
algorithms, aims to create concise and accurate summaries, thereby significantly reducing …

Large language models (LLMs), even when specifically trained to process long input
contexts, struggle to capture relevant information located in the middle of their input. This …

While large language models (LLMs) like ChatGPT have shown impressive capabilities in
Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this …

We characterize and study zero-shot abstractive summarization in Large Language Models
(LLMs) by measuring position bias, which we propose as a general formulation of the more …

Question-answering for domain-specific applications has recently attracted much interest
due to the latest advancements in large language models (LLMs). However, accurately …

With the popularity of large language models (LLMs) and their ability to handle longer input
documents, there is a growing need for high-quality long document summarization datasets …

Recent developments in Japanese large language models (LLMs) primarily focus on
general domains, with fewer advancements in Japanese biomedical LLMs. One obstacle is …

Scientific document summarization has been a challenging task due to the long structure of
the input text. The long input hinders the simultaneous effective modeling of both global high …

Text summarization research has undergone several significant transformations with the
advent of deep neural networks, pre-trained language models (PLMs), and recent large …