How can I implement a BERT-based text summarizer using TensorFlow

Question

With the help of Python programming, can you explain how to implement a BERT-based text summarizer using TensorFlow?

Answer 1

To implement a BERT-based text summarizer in TensorFlow, you can use Hugging Face's Transformers library with TensorFlow. Here is the code you can refer to:

In the above, we are using approaches like:

• Load Pre-trained BERT:

  • Use Hugging Face's TFBertForSequenceClassification for initial setup.
  • Fine-tune a BERT model on a summarization dataset (e.g., CNN/DailyMail).

• Preprocess Input Text:

  • Tokenize the text using BertTokenizer.
  • Ensure the input is truncated or padded to the model's maximum sequence length.

• Generate Summary:

  • Feed the input to the model.
  • Decode the output logits into human-readable text.

• Fine-tune for Summarization:

  • Use a summarization-specific dataset for fine-tuning BERT or switch to TFBartForConditionalGeneration for better results, as BERT itself is not directly optimized for summarization.

Hence, this code serves as a starting point, but for high-quality summarization, consider fine-tuning the model or using a pre-trained summarization model like Facebook/bart-large-cnn or t5-small.