Summary of Assessment Of Transformer-based Encoder-decoder Model For Human-like Summarization, by Sindhu Nair et al.
Assessment of Transformer-Based Encoder-Decoder Model for Human-Like Summarization
by Sindhu Nair, Y.S. Rao, Radha Shankarmani
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the use of a transformer-based BART model for automatic text summarization, which is an open-ended problem with many challenges. The researchers leverage the encoder-decoder framework in deep learning and fine-tune their model on diverse sample articles to assess the quality of summaries based on human evaluation parameters. They also compare the finetuned model’s performance to a baseline pretrained model using metrics like ROUGE score and BERTScore. Additionally, they explore domain adaptation for improved abstractive summarization of dialogues between interlocutors. The study finds that popular evaluation metrics are insensitive to factual errors and investigates summaries generated by the finetuned model using contemporary evaluation metrics like WeCheck and SummaC. Empirical results on BBC News articles show that human-written gold standard summaries are more factually consistent than abstractive summaries generated by the finetuned model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make computers better at summarizing long texts, which can help people make decisions faster. It uses a special kind of artificial intelligence called deep learning and a technique called transformer-based BART models. The researchers train this model on many articles and test it to see how well it does. They also compare it to a simpler model and find that the new model is better at creating summaries that are accurate and make sense. The study also shows that current ways of measuring summary quality might not be good enough because they don’t account for mistakes in the information. |
Keywords
» Artificial intelligence » Deep learning » Domain adaptation » Encoder decoder » Rouge » Summarization » Transformer
