Search Results for author: Ahmed Magooda

Found 9 papers, 2 papers with code

Exploring Multitask Learning for Low-Resource Abstractive Summarization

1 code implementation Findings (EMNLP) 2021 Ahmed Magooda, Diane Litman, Mohamed Elaraby

In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning.

Abstractive Text Summarization Extractive Summarization +1

Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization

no code implementations Findings (EMNLP) 2021 Ahmed Magooda, Diane Litman

This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data.

Abstractive Text Summarization Data Augmentation +1

Exploring Multitask Learning for Low-Resource AbstractiveSummarization

no code implementations17 Sep 2021 Ahmed Magooda, Mohamed Elaraby, Diane Litman

In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning.

Abstractive Text Summarization Extractive Summarization +1

Abstractive Summarization for Low Resource Data using Domain Transfer and Data Synthesis

no code implementations9 Feb 2020 Ahmed Magooda, Diane Litman

Evaluations demonstrated that summaries produced by the tuned model achieved higher ROUGE scores compared to model trained on just student reflection data or just newspaper data.

Abstractive Text Summarization Extractive Summarization

Attend to the beginning: A study on using bidirectional attention for extractive summarization

1 code implementation9 Feb 2020 Ahmed Magooda, Cezary Marcjan

Forum discussion data differ in both structure and properties from generic form of textual data such as news.

Extractive Summarization

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