Summary of Improving Sequence-to-sequence Models For Abstractive Text Summarization Using Meta Heuristic Approaches, by Aditya Saxena et al.
Improving Sequence-to-Sequence Models for Abstractive Text Summarization Using Meta Heuristic Approaches
by Aditya Saxena, Ashutosh Ranjan
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 A neural abstractive text summarization model is developed by proposing innovative strategies to enhance current sequence-to-sequence (seq2seq) architectures, allowing them to handle issues like saliency, familiarity, and human lucidness. This paper aims to fine-tune hyper-parameters and explore specific encoder-decoder combinations for improved summarization performance on the CNN/DailyMail dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers worked together to create a new way to summarize news articles quickly and efficiently. They used special kinds of artificial intelligence models called sequence-to-sequence models, which are great at turning long pieces of text into shorter summaries. The goal was to make these models better by trying different combinations of parts and adjusting some settings. The team tested their ideas on a big dataset of news articles from CNN and DailyMail. |
Keywords
* Artificial intelligence * Cnn * Encoder decoder * Seq2seq * Summarization
