9 citations · 13 across the 11 of their papers we have counts for
12 papers
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…
Proposition-Level Clustering for Multi-Document Summarization
Ori Ernst, Avi Caciularu, Ori Shapira +4
Text clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition. Particularly, cluste…
Extending Multi-Text Sentence Fusion Resources via Pyramid Annotations
Daniela Brook Weiss, Paul Roit, Ori Ernst +1
NLP models that compare or consolidate information across multiple documents often struggle when challenged with recognizing substantial information redundancies across the texts.…
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions
Daniela Brook Weiss, Paul Roit, Ayal Klein +2
Multi-text applications, such as multi-document summarization, are typically required to model redundancies across related texts. Current methods confronting consolidation struggle…
iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration
Eran Hirsch, Alon Eirew, Ori Shapira +7
We introduce iFacetSum, a web application for exploring topical document sets. iFacetSum integrates interactive summarization together with faceted search, by providing a novel fac…
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
Ori Ernst, Ori Shapira, Ramakanth Pasunuru +4
Aligning sentences in a reference summary with their counterparts in source documents was shown as a useful auxiliary summarization task, notably for generating training data for s…
Speech Dereverberation Using Fully Convolutional Networks
Ori Ernst, Shlomo E. Chazan, Sharon Gannot +1
Speech derverberation using a single microphone is addressed in this paper. Motivated by the recent success of the fully convolutional networks (FCN) in many image processing appli…