arXiv AI June 19, 2026 By Vu Nguyen Nguyen Xuan, Huy Ngo Quang

A BART-based approach with hierarchical strategy for Vietnamese abstractive multi-document summarization

arXiv:2606. 19591v1 Announce Type: cross Abstract: In this technical report, we focus on solving the challenge of Vietnamese multi-document abstractive summarization, introduced in the International Workshop on Vietnamese Language and Speech Processing (VLSP) 2022.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 3

A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs

arXiv:2606. 03867v1 Announce Type: cross Abstract: Multi-Document Summarization (MDS) plays a critical role in distilling essential information from collections of textual data.

By Cuong Vuong Tuan, Trang Mai Xuan, Tien-Cuong Nguyen, Vu-Duc Ngo, Thien Van Luong

llms agents nlp fine-tuning benchmarks

arXiv AI

Jul 14

Abstractiveness Metrics for Evaluating Text Summarization: A Refined Formulation with Empirical Validation

arXiv:2607. 10806v1 Announce Type: cross Abstract: Quantifying abstractiveness in generated summaries is essential for evaluating summarization models beyond surface-level metrics like ROUGE.

By Praveenkumar Katwe, Rakesh Chandra Balabantaray, Kali Prasad Vittala

nlp safety

arXiv AI

Jul 14

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization

arXiv:2606. 05494v3 Announce Type: replace-cross Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information.

By Ahmed Alansary, Ali Hamdi

llms nlp fine-tuning

arXiv AI

Jun 6

MASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization

arXiv:2606. 05494v1 Announce Type: cross Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information.

By Ahmed Alansary, Ali Hamdi

llms nlp fine-tuning

arXiv AI

Jun 9

Segment-level Tree Search for Long Meeting Document Summarization

arXiv:2606. 08445v1 Announce Type: cross Abstract: Meeting documents are challenging to summarize due to their length and complex conversational structure.

By Sangwon Ryu, Heejin Do, Jun Seo, Daehui Kim, Yunsu Kim, Gary Geunbae Lee, Jungseul Ok

nlp

arXiv AI

Aug 6

MIDAS: Multi-LLM Iterative Data-Adaptive Summarization

arXiv:2608. 04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult.

By Karen Lee, Dhanashree Balaram, Seojun Shon, Umair Rasheed

llms nlp benchmarks

Related stories

A Training-Free Mixture-of-Agents Framework for Multi-Document Summarization using LLMs and Knowledge Graphs

Abstractiveness Metrics for Evaluating Text Summarization: A Refined Formulation with Empirical Validation

A Multi-Model Metric-based Selection Framework for Abstractive Text summarization

MASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization

Segment-level Tree Search for Long Meeting Document Summarization

MIDAS: Multi-LLM Iterative Data-Adaptive Summarization