10 citations · 35 across the 19 of their papers we have counts for
20 papers
CoWriter: Guiding Methodological and Synergetic Human-LLM Co-Authoring
Shmuel Amar, Benjamin Lieber, Ori Shapira +4
While LLMs have transformed text generation, standard chat-and-canvas interfaces often reduce human authoring to quite passive supervision, causing review fatigue, over-reliance on…
The Overlooked Role of Graded Relevance Thresholds in Multilingual Dense Retrieval
Tomer Wullach, Ori Shapira, Amir DN Cohen
Dense retrieval models are typically fine-tuned with contrastive learning objectives that require binary relevance judgments, even though relevance is inherently graded. We analyze…
Consensus or Conflict? Fine-Grained Evaluation of Conflicting Answers in Question-Answering
Eviatar Nachshoni, Arie Cattan, Shmuel Amar +2
Large Language Models (LLMs) have demonstrated strong performance in question answering (QA) tasks. However, Multi-Answer Question Answering (MAQA), where a question may have sever…
A Unifying Scheme for Extractive Content Selection Tasks
Shmuel Amar, Ori Shapira, Aviv Slobodkin +1
A broad range of NLP tasks involve selecting relevant text spans from given source texts. Despite this shared objective, such \textit{content selection} tasks have traditionally be…
Information Types in Product Reviews
Ori Shapira, Yuval Pinter
Information in text is communicated in a way that supports a goal for its reader. Product reviews, for example, contain opinions, tips, product descriptions, and many other types o…
Measuring the Effect of Transcription Noise on Downstream Language Understanding Tasks
Ori Shapira, Shlomo E. Chazan, Amir DN Cohen
With the increasing prevalence of recorded human speech, spoken language understanding (SLU) is essential for its efficient processing. In order to process the speech, it is common…
Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs
Shadi Iskander, Nachshon Cohen, Zohar Karnin +2
Training large language models (LLMs) for external tool usage is a rapidly expanding field, with recent research focusing on generating synthetic data to address the shortage of av…
SEAM: A Stochastic Benchmark for Multi-Document Tasks
Gili Lior, Avi Caciularu, Arie Cattan +3
Various tasks, such as summarization, multi-hop question answering, or coreference resolution, are naturally phrased over collections of real-world documents. Such tasks present a…
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…
Multi-Review Fusion-in-Context
Aviv Slobodkin, Ori Shapira, Ran Levy +1
Grounded text generation, encompassing tasks such as long-form question-answering and summarization, necessitates both content selection and content consolidation. Current end-to-e…
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…
SummHelper: Collaborative Human-Computer Summarization
Aviv Slobodkin, Niv Nachum, Shmuel Amar +2
Current approaches for text summarization are predominantly automatic, with rather limited space for human intervention and control over the process. In this paper, we introduce Su…
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…
Multi-Document Keyphrase Extraction: Dataset, Baselines and Review
Ori Shapira, Ramakanth Pasunuru, Ido Dagan +1
Keyphrase extraction has been extensively researched within the single-document setting, with an abundance of methods, datasets and applications. In contrast, multi-document keyphr…
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…
Evaluating Interactive Summarization: an Expansion-Based Framework
Ori Shapira, Ramakanth Pasunuru, Hadar Ronen +3
Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization…
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…
Massive Multi-Document Summarization of Product Reviews with Weak Supervision
Ori Shapira, Ran Levy
Product reviews summarization is a type of Multi-Document Summarization (MDS) task in which the summarized document sets are often far larger than in traditional MDS (up to tens of…
Better Rewards Yield Better Summaries: Learning to Summarise Without References
Florian Böhm, Yang Gao, Christian M. Meyer +3
Reinforcement Learning (RL) based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during…
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Ori Shapira, David Gabay, Yang Gao +5
Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries…