Text Summarization & Large Language Models

Large language models can perform various operations such as text summarization, sentiment analysis, language translation and text-based recommendation systems.

With the introduction of transformer architecture, thenatural language processing tasks are made easy which allows the model toprocess an entire sentence or paragraph at once, rather than each word at atime. The processing of text input with the transformer architecture is basedon tokenization, which is the process of transforming texts into smaller components called tokens. These can be words, subwords, characters, or many others. With the power of transfer learning we can enhance a specific task bytraining it in a specific dataset aka fine-tunning.

In this text summariztion prototype, we are using BART model which is a Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is fed the original tokens. This model learned to generate concise summaries ofl ong text passages. We evaluated the model's performance using metrics like ROUGE score, which measures how well the generated summaries match the original text. After training, we saved the model and tokenizer to disk for future use.

This technology has wide-ranging applications, from simplifying long articles to information retrieval and decision-making processes. By automating the summarization process, we can savetime and resources while still capturing the essential information conveyed inthe original text.


Model Type:

  • BERT: Encoder Only
  • GPT: Decoder Only
  • BART: Encoder-Decoder


  • BERT: Bidirectional
  • GPT: Unidirectional (left-to-right)
  • BART: Bidirectional


  • BERT: Masked language modeling (MLM)
  • GPT: Autoregressive (causal) language modeling
  • BART: Span Corruption (Masking entire spans of words)


  • BERT: Language modeling (Masking entire spans of words)
  • GPT: Language modeling (predicting the next word in a sequence)
  • BART: Versatile and can be used for various NLP tasks


  • BERT: Task-specific layer added on top of the pre-trained BERT model
  • GPT: Providing task-specific prompts using few-shot or one-shot adaptation and adapting the model's parameters
  • BART: Versatile and can be used for various NLP tasks

Use Case:

  • BERT: Sentiment Analysis, Named Entity Recognition, Word Classification
  • GPT: Text generation, Creative writing, Text completion
  • BART: Translation, Text Summarization, Question & Answer

Tags: generative ai, text summarization, natural language processing, BART, large language models

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