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Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language

arXiv 2403.09719 ↗First posted 12 Mar 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

In this paper, we introduce summarization MevakerSumm and conclusion extraction MevakerConc datasets for the Hebrew language based on the State Comptroller and Ombudsman of Israel reports, along with two auxiliary datasets. We accompany these datasets with models for conclusion extraction (HeConE, HeConEspc) and conclusion allocation (HeCross). All of the code, datasets, and model checkpoints used in this work are publicly available.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 12 Mar 2024.

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References · 8

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  3. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  4. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
  5. 2020Text Segmentation by Cross Segment Attentioncited by 54
  6. 2020Neural Modeling for Named Entities and Morphology (NEMO^2)cited by 26
  7. 2021ParaShoot: A Hebrew Question Answering Datasetcited by 12
  8. 2023HeRo: RoBERTa and Longformer Hebrew Language Modelscited by 12

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