Papers by Elozino Egonmwan
Transformer-based Model for Single Documents Neural Summarization (D19-56)
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| Challenge: | Existing approaches for document summarization use manual feature engineering, integer linear programming and data-driven approaches. |
| Approach: | They propose a framework that encodes the source text first with a transformer, then a sequence-to-sequence model. |
| Outcome: | The proposed framework improves performance on extractive and abstractive document summarization task using the CNN/DailyMail and Newsroom datasets. |
Transformer and seq2seq model for Paraphrase Generation (D19-56)
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| Challenge: | Existing methods for generating paraphrases fall into one of these broad categories -rule-based, seq2seq, deep generative models and a varied combination. |
| Approach: | They propose a framework that combines transformer and sequence-to-sequence models for better quality of generated paraphrases. |
| Outcome: | The proposed framework improves on two datasets-QUORA and MSCOCO using transformer and sequence-to-sequence models. |
Cross-Task Knowledge Transfer for Query-Based Text Summarization (D19-58)
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| Challenge: | Existing methods for summarization data corpora are limited to extractive and abstractive summarizing. |
| Approach: | They propose to use machine reading comprehension (MRC) and query-based text summarization to produce extractive and abstractive summaries from pre-trained MRC and MT models. |
| Outcome: | The proposed model outperforms existing methods on CNN/Daily Mail and Debatepedia datasets and can be used as a baseline for future systems. |