Summary of Survey on Abstractive Text Summarization: Dataset, Models, and Metrics, by Gospel Ozioma Nnadi and Flavio Bertini
Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
by Gospel Ozioma Nnadi, Flavio Bertini
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the advancements in natural language processing (NLP) tasks such as machine translation, text classification, and text summarization, leveraging transformer models with attention mechanisms, pretraining on general knowledge, and fine-tuning for downstream tasks. The transformers’ capabilities are particularly showcased in abstractive summarization, where sections of a source document are paraphrased to produce summaries that closely resemble human expression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how transformer models have improved various NLP tasks like machine translation, text classification, and summarization. It also talks about response generation and image-to-text tasks like captioning. The transformers’ attention mechanisms, pretraining on general knowledge, and fine-tuning for specific tasks make them really good at certain jobs. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Natural language processing » Nlp » Pretraining » Summarization » Text classification » Transformer » Translation
