9 citations · 11 across the 7 of their papers we have counts for
9 papers · 1 filter
Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics
Lorenzo Jaime Yu Flores, Ori Ernst, Jackie Chi Kit Cheung
Well-calibrated model confidence scores can improve the usefulness of text generation models. For example, users can be prompted to review predictions with low confidence scores, t…
PreSumm: Predicting Summarization Performance Without Summarizing
Steven Koniaev, Ori Ernst, Jackie Chi Kit Cheung
Despite recent advancements in automatic summarization, state-of-the-art models do not summarize all documents equally well, raising the question: why? While prior research has ext…
The Power of Summary-Source Alignments
Ori Ernst, Ori Shapira, Aviv Slobodkin +5
Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection, followed by text generation. In this context, alignment…
OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
Shmuel Amar, Liat Schiff, Ori Ernst +3
The performance of automatic summarization models has improved dramatically in recent years. Yet, there is still a gap in meeting specific information needs of users in real-world…
Controlled Text Reduction
Aviv Slobodkin, Paul Roit, Eran Hirsch +2
Producing a reduced version of a source text, as in generic or focused summarization, inherently involves two distinct subtasks: deciding on targeted content and generating a coher…
How "Multi" is Multi-Document Summarization?
Ruben Wolhandler, Arie Cattan, Ori Ernst +1
The task of multi-document summarization (MDS) aims at models that, given multiple documents as input, are able to generate a summary that combines disperse information, originally…