Shmuel Amar


2023

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OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization
Shmuel Amar | Liat Schiff | Ori Ernst | Asi Shefer | Ori Shapira | Ido Dagan
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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 scenarios, particularly when a targeted summary is sought, such as in the useful aspect-based summarization setting targeted in this paper. Previous datasets and studies for this setting have predominantly concentrated on a limited set of pre-defined aspects, focused solely on single document inputs, or relied on synthetic data. To advance research on more realistic scenarios, we introduce OpenAsp, a benchmark for multi-document open aspect-based summarization. This benchmark is created using a novel and cost-effective annotation protocol, by which an open aspect dataset is derived from existing generic multi-document summarization datasets. We analyze the properties of OpenAsp showcasing its high-quality content. Further, we show that the realistic open-aspect setting realized in OpenAsp poses a challenge for current state-of-the-art summarization models, as well as for large language models.

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SummHelper: Collaborative Human-Computer Summarization
Aviv Slobodkin | Niv Nachum | Shmuel Amar | Ori Shapira | Ido Dagan
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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 SummHelper, and screencast demo at https://www.youtube.com/watch?v=nGcknJwGhxk a 2-phase summarization assistant designed to foster human-machine collaboration. The initial phase involves content selection, where the system recommends potential content, allowing users to accept, modify, or introduce additional selections. The subsequent phase, content consolidation, involves SummHelper generating a coherent summary from these selections, which users can then refine using visual mappings between the summary and the source text. Small-scale user studies reveal the effectiveness of our application, with participants being especially appreciative of the balance between automated guidance and opportunities for personal input.

2021

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Teach the Rules, Provide the Facts: Targeted Relational-knowledge Enhancement for Textual Inference
Ohad Rozen | Shmuel Amar | Vered Shwartz | Ido Dagan
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

We present InferBert, a method to enhance transformer-based inference models with relevant relational knowledge. Our approach facilitates learning generic inference patterns requiring relational knowledge (e.g. inferences related to hypernymy) during training, while injecting on-demand the relevant relational facts (e.g. pangolin is an animal) at test time. We apply InferBERT to the NLI task over a diverse set of inference types (hypernymy, location, color, and country of origin), for which we collected challenge datasets. In this setting, InferBert succeeds to learn general inference patterns, from a relatively small number of training instances, while not hurting performance on the original NLI data and substantially outperforming prior knowledge enhancement models on the challenge data. It further applies its inferences successfully at test time to previously unobserved entities. InferBert is computationally more efficient than most prior methods, in terms of number of parameters, memory consumption and training time.