# Mevaker: Conclusion Extraction and Allocation Resources for the Hebrew Language

arXiv: https://arxiv.org/abs/2403.09719 · HTML: https://mlsanity.com/paper/2403.09719/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Text Segmentation by Cross Segment Attention](https://mlsanity.com/paper/2004.14535/) (2020)
- [Neural Modeling for Named Entities and Morphology (NEMO^2)](https://mlsanity.com/paper/2007.15620/) (2020)
- [ParaShoot: A Hebrew Question Answering Dataset](https://mlsanity.com/paper/2109.11314/) (2021)
- [HeRo: RoBERTa and Longformer Hebrew Language Models](https://mlsanity.com/paper/2304.11077/) (2023)

## 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.