Summary of Investigating Text Shortening Strategy in Bert: Truncation Vs Summarization, by Mirza Alim Mutasodirin et al.
Investigating Text Shortening Strategy in BERT: Truncation vs Summarization
by Mirza Alim Mutasodirin, Radityo Eko Prasojo
First submitted to arxiv on: 19 Mar 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 study investigates the performance of document truncation and summarization in text classification tasks, exploring their effectiveness as alternatives to overcome the input max-length limitation of Transformer-based models. The researchers used a dataset of summarization tasks based on Indonesian news articles (IndoSum) and found that summarization outperformed most truncation method variations, with the best strategy being taking the head of the document or using extractive summarization. The study demonstrates the potential of document summarization as a shortening alternative and provides publicly available code and data for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make machines better at understanding text by cutting it down to size. They compared two ways to shorten text: chopping off bits from the start or end, or using special software to summarize what’s most important. The researchers found that summarization worked best, especially when they took just the most important parts of the text. This could be a new way for machines to learn and understand text without getting overwhelmed by too much information. |
Keywords
» Artificial intelligence » Summarization » Text classification » Transformer
