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

4 citations · 6 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CL2023★ 2 cited

Generating Query Focused Summaries without Fine-tuning the Transformer-based Pre-trained Models

Deen Abdullah, Shamanth Nayak, Gandharv Suri +1

Fine-tuning the Natural Language Processing (NLP) models for each new data set requires higher computational time associated with increased carbon footprint and cost. However, fine…

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…