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Extractive Text Summarization on Single Documents Using Deep Learning
The task of summarization can be categorized into two methods, extractive and abstractive summarization. Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the summary in its own words. The task of g...
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| Format: | Thesis |
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AUC Knowledge Fountain
2022
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| _version_ | 1867613421051052032 |
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| access_status_str | Open Access |
| author | Mohamed, Shehab Mostafa Abdel-Salam |
| author_browse | Mohamed, Shehab Mostafa Abdel-Salam |
| author_facet | Mohamed, Shehab Mostafa Abdel-Salam |
| author_sort | Mohamed, Shehab Mostafa Abdel-Salam |
| collection | Thesis |
| description | The task of summarization can be categorized into two methods, extractive and abstractive summarization. Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches such as statistical-based, graph-based, and deep-learning based approaches. Deep learning has achieved promising performance in comparison with the classical approaches and with the evolution of neural networks such as the attention network or commonly known as the Transformer architecture, there are potential areas for improving the summarization approach. The introduction of transformers and its encoder model BERT, has created advancement in the performance of many downstream tasks in NLP, including the summarization task. The objective of this thesis is to study the performance of deep learning-based models on text summarization through a series of experiments, and propose “SqueezeBERTSum”, a trained summarization model fine-tuned with the SqueezeBERT encoder which achieved competitive ROUGE scores retaining original BERT model’s performance by 98% with ~49% fewer trainable parameters. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-2872 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:51.500Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-2872 Extractive Text Summarization on Single Documents Using Deep Learning Mohamed, Shehab Mostafa Abdel-Salam The task of summarization can be categorized into two methods, extractive and abstractive summarization. Extractive approach selects highly meaningful sentences to form a summary while the abstractive approach interprets the original document and generates the summary in its own words. The task of generating a summary, whether extractive or abstractive, has been studied with different approaches such as statistical-based, graph-based, and deep-learning based approaches. Deep learning has achieved promising performance in comparison with the classical approaches and with the evolution of neural networks such as the attention network or commonly known as the Transformer architecture, there are potential areas for improving the summarization approach. The introduction of transformers and its encoder model BERT, has created advancement in the performance of many downstream tasks in NLP, including the summarization task. The objective of this thesis is to study the performance of deep learning-based models on text summarization through a series of experiments, and propose “SqueezeBERTSum”, a trained summarization model fine-tuned with the SqueezeBERT encoder which achieved competitive ROUGE scores retaining original BERT model’s performance by 98% with ~49% fewer trainable parameters. 2022-01-31T08:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/1853 https://fount.aucegypt.edu/context/etds/article/2872/viewcontent/shehab_mostafa_abdelsalam_mohamed_thesis.pdf Theses and Dissertations AUC Knowledge Fountain text summarization deep learning transformer architecture neural networks bert extractive summarization Other Computer Engineering |
| spellingShingle | text summarization deep learning transformer architecture neural networks bert extractive summarization Other Computer Engineering Mohamed, Shehab Mostafa Abdel-Salam Extractive Text Summarization on Single Documents Using Deep Learning |
| title | Extractive Text Summarization on Single Documents Using Deep Learning |
| title_full | Extractive Text Summarization on Single Documents Using Deep Learning |
| title_fullStr | Extractive Text Summarization on Single Documents Using Deep Learning |
| title_full_unstemmed | Extractive Text Summarization on Single Documents Using Deep Learning |
| title_short | Extractive Text Summarization on Single Documents Using Deep Learning |
| title_sort | extractive text summarization on single documents using deep learning |
| topic | text summarization deep learning transformer architecture neural networks bert extractive summarization Other Computer Engineering |
| url | https://fount.aucegypt.edu/etds/1853 https://fount.aucegypt.edu/context/etds/article/2872/viewcontent/shehab_mostafa_abdelsalam_mohamed_thesis.pdf |
| work_keys_str_mv | AT mohamedshehabmostafaabdelsalam extractivetextsummarizationonsingledocumentsusingdeeplearning |