San-BERT: Extractive Summarization for Sanskrit Documents using BERT and it's variants
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Abstract (arXiv)
In this work, we develop language models for the Sanskrit language, namely Bidirectional Encoder Representations from Transformers (BERT) and its variants: A Lite BERT (ALBERT), and Robustly Optimized BERT (RoBERTa) using Devanagari Sanskrit text corpus. Then we extracted the features for the given text from these models. We applied the dimensional reduction and clustering techniques on the features to generate an extractive summary for a given Sanskrit document. Along with the extractive text summarization techniques, we have also created and released a Sanskrit Devanagari text corpus publicly.
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References · 10
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
- 2016SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documentscited by 245
- 2018Ranking Sentences for Extractive Summarization with Reinforcement Learningcited by 128
- 2019Fine-tune BERT for Extractive Summarizationcited by 122
- 2017Text Summarization Techniques: A Brief Surveycited by 59
- 2021Evaluating Neural Word Embeddings for Sanskritcited by 8
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