Extractive summarization is a crucial task in natural language processing that aims to
condense long documents into shorter versions by directly extracting sentences. The recent …

Text summarization systems have made significant progress in recent years, but typically
generate summaries in one single step. However, the one-shot summarization setting is …

Extractive summarization aims to form a summary by directly extracting sentences from the
source document. Existing works mostly formulate it as a sequence labeling problem by …

Despite the recent progress in language generation models, their outputs may not always
meet user expectations. In this work, we study whether informational feedback in natural …

Extractive summarization for long documents is challenging due to the extended structured
input context. The long-distance sentence dependency hinders cross-sentence relations …

Despite significant progress in understanding and improving faithfulness in abstractive
summarization, the question of how decoding strategies affect faithfulness is less studied …

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 …

Retrieval-augmented generation framework can address the limitations of large language
models by enabling real-time knowledge updates for more accurate answers. An efficient …

In the rapidly evolving landscape of medical research, accurate and concise summarization
of clinical studies is crucial to support evidence-based practice. This paper presents a novel …

Multi-document Summarization (MDS) characterizes compressing information from multiple
source documents to its succinct summary. An ideal summary should encompass all topics …