3 citations · 6 across the 4 of their papers we have counts for
6 papers · 1 filter
An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation
Matan Orbach, Ohad Eytan, Benjamin Sznajder +12
Optimizing Retrieval-Augmented Generation (RAG) configurations for specific tasks is a complex and resource-intensive challenge. Motivated by this challenge, frameworks for RAG hyp…
JuStRank: Benchmarking LLM Judges for System Ranking
Ariel Gera, Odellia Boni, Yotam Perlitz +3
Given the rapid progress of generative AI, there is a pressing need to systematically compare and choose between the numerous models and configurations available. The scale and ver…
HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
Odellia Boni, Guy Feigenblat, Guy Lev +3
We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS), which targets the use-case of generating actionable instructions…
A Study of Human Summaries of Scientific Articles
Odellia Boni, Guy Feigenblat, Doron Cohen +2
Researchers and students face an explosion of newly published papers which may be relevant to their work. This led to a trend of sharing human summaries of scientific papers. We an…
A Summarization System for Scientific Documents
Shai Erera, Michal Shmueli-Scheuer, Guy Feigenblat +15
We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, explorat…
Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-based Extractive Summarization
Haggai Roitman, Guy Feigenblat, David Konopnicki +2
We propose Dual-CES -- a novel unsupervised, query-focused, multi-document extractive summarizer. Dual-CES is designed to better handle the tradeoff between saliency and focus in s…