most citedCombining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization

4 citations · 4 across the 2 of their papers we have counts for

collaborators

3 papers

cs.CL2026

Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets

Yllias Chali, Deen Abdullah

Large-scale datasets are widely used to perform summarization tasks, but they may not include queries alongside documents and summaries. In the search for suitable datasets for Que…

cs.CL2026

Text Summarization With Graph Attention Networks

Mohammadreza Ardestani, Yllias Chali

This study aimed to leverage graph information, particularly Rhetorical Structure Theory (RST) and Co-reference (Coref) graphs, to enhance the performance of our baseline summariza…

cs.CL2022★ 4 cited

Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization

David Adams, Gandharv Suri, Yllias Chali

In Natural Language Processing, multi-document summarization (MDS) poses many challenges to researchers above those posed by single-document summarization (SDS). These challenges i…