Papers by Mohamed Elaraby
Exploring Multitask Learning for Low-Resource Abstractive Summarization (2021.findings-emnlp)
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| Challenge: | Recent work shows that training text encoders using data from multiple tasks helps to produce an encoder that can be used in numerous downstream tasks with minimal fine-tuning. |
| Approach: | They incorporate four different tasks to improve abstractive summarization performance . they use a pretrained BERT model and train all tasks using a small-scale training corpus . |
| Outcome: | The proposed model outperforms a model trained in a multitask setting with no additional summarization data. |
ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs (2026.eacl-long)
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| Challenge: | Argument Representation Coverage (ARC) assesses how well summaries preserve salient arguments . despite their fluency, LLMs frequently hallucinate or omit key content . |
| Approach: | They propose an evaluation framework that assesses how well summaries preserve salient arguments . they use argument representation coverage to distinguish between different information types . |
| Outcome: | The proposed framework assesses how well summaries preserve salient arguments . the authors show that LLMs capture some salient roles but omit critical information . |
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining (2022.coling-1)
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| Challenge: | Existing abstractive summarization models do not take into account argumentative structure of legal documents, which poses a challenge towards effective abstractive summary. |
| Approach: | They propose a technique that integrates argument role labeling into the summarization process by integrating argument role labels into the document. |
| Outcome: | The proposed method improves over strong baselines with pretrained language models. |
Persuasiveness of Generated Free-Text Rationales in Subjective Decisions: A Case Study on Pairwise Argument Ranking (2024.findings-emnlp)
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| Challenge: | Existing research on generating free-text rationales has focused on tasks where there is an expected factual ground truth. |
| Approach: | They analyze generated free-text rationales in tasks with subjective answers . they find open-source LLMs generate highly persuasive rationale models . |
| Outcome: | The proposed model outperforms closed-source models in pairwise argument ranking, a highly subjective task with potential for debate assistance. |
You Tweet What You Speak: A City-Level Dataset of Arabic Dialects (L18-1)
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| Challenge: | Existing studies of Arabic dialects have focused on blogs and comments on online news sites, but data on other dialects are costly and limited. |
| Approach: | They present a dataset of > 1/4 billion tweets representing a wide range of Arabic dialects. |
| Outcome: | The dataset represents 29 major Arab cities from 10 Arab countries with varying dialects. |
Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking (2023.findings-acl)
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| Challenge: | Existing summarization models struggle to accurately capture the main arguments of long legal opinions, leading to suboptimal summaries. |
| Approach: | They propose a framework for abstractive summarization of long legal opinions that takes into account the argument structure of the document and reranks them based on alignment with the document's argument structure. |
| Outcome: | The proposed approach outperforms several strong baselines on a dataset of long legal opinions and outperformed existing models. |
ReflectSumm: A Benchmark for Course Reflection Summarization (2024.lrec-main)
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| Challenge: | Existing research has focused on standard summarization benchmarks within domains like news, scientific articles, and opinions. |
| Approach: | They propose a summarization dataset specifically designed for summarizing students’ reflective writing. |
| Outcome: | The proposed summarization dataset can be used in opinion summarizing scenarios and in educational domains. |