Post
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π’ Have you ever been wondered how specifically Transformers were capable for handling long input contexts?
I got a chance to tackle this through long document texts summarization problem, and delighted to share the related survey and diagram for a quick skimming below:
Preprint π https://nicolay-r.github.io/website/data/preprint-AINL_2023_longt5_summarization.pdf
Springer π https://link.springer.com/article/10.1007/s10958-024-07435-z
π― The aim of the survey was the development of the long-document summarizer for mass-media news in Vietnamese language. π»π³
Sharing for a quick skimming of the methods performance overview of various LM-based solution across several datasets, covering domain-oriented advances in Vietnamese language (see attached screenshots)
As for solution we consider:
βοΈ 1. Adapt existed google/pegasus-cnn_dailymail for summarizing large dataset for arranging training
βοΈ 2. Tuning google/long-t5-tglobal-large suitable for performing generative summarization.
Implementation details:
π https://github.com/nicolay-r/ViLongT5
(Simplier to go with huggingface rather flaxformer that so far become a legacy engine)
I got a chance to tackle this through long document texts summarization problem, and delighted to share the related survey and diagram for a quick skimming below:
Preprint π https://nicolay-r.github.io/website/data/preprint-AINL_2023_longt5_summarization.pdf
Springer π https://link.springer.com/article/10.1007/s10958-024-07435-z
π― The aim of the survey was the development of the long-document summarizer for mass-media news in Vietnamese language. π»π³
Sharing for a quick skimming of the methods performance overview of various LM-based solution across several datasets, covering domain-oriented advances in Vietnamese language (see attached screenshots)
As for solution we consider:
βοΈ 1. Adapt existed google/pegasus-cnn_dailymail for summarizing large dataset for arranging training
βοΈ 2. Tuning google/long-t5-tglobal-large suitable for performing generative summarization.
Implementation details:
π https://github.com/nicolay-r/ViLongT5
(Simplier to go with huggingface rather flaxformer that so far become a legacy engine)