Papers by Yllias Chali
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. |
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion (C18-1)
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| Challenge: | a new method for abstractive summarization is being developed for document summarizing . abstractive methods require extensive natural language generation to rewrite the sentences . |
| Approach: | They propose an unsupervised abstractive summarization system in multi-document context . they use a paraphrastic sentence fusion model which performs sentence synthesis and paraphrazing . |
| Outcome: | The proposed model improves information coverage and abstractiveness of generated sentences. |