This paper provides a survey of the emerging area of Large Language Models (LLMs) for
Software Engineering (SE). It also sets out open research challenges for the application of …

Background Medical texts present significant domain-specific challenges, and manually
curating these texts is a time-consuming and labor-intensive process. To address this …

Given the overwhelming and rapidly increasing volumes of the published biomedical
literature, automatic biomedical text summarization has long been a highly important task …

Recent advances in the field of large language models (LLMs) underline their high potential
for applications in a variety of sectors. Their use in healthcare, in particular, holds out …

We assessed the performance of large language models' summarizing clinical dialogues
using computational metrics and human evaluations. The comparison was done between …

Objectives This study addresses the critical need for accurate summarization in radiology by
comparing various Large Language Model (LLM)-based approaches for automatic summary …

Biomedical multi-document summarization (BioMDSum) involves automatically generating
concise and informative summaries from collections of related biomedical documents. While …

User stories, expressed in snippets of natural language text, are commonly used to elicit
stakeholder's needs in agile software development. Requirement engineers model user …

Abstractive summarization involves generating a coherent and concise summary that
captures the main topics of the original document. In this work, we address the task of …