What can you do to improve output fluency when generating text summaries with a language model

Question

Can i know What can you do to improve output fluency when generating text summaries with a language model?

Answer 1

Output fluency in text summarization can be improved by fine-tuning on high-quality, well-structured datasets, using beam search or top-k sampling, and applying length penalties or repetition penalties.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses the BART model fine-tuned for summarization, known for fluent text generation.
• Applies beam search to improve fluency and coherence of the output.
• Uses length penalty and early stopping to avoid overly short or repetitive summaries.

Hence, by combining high-quality models, advanced decoding strategies, and careful tuning of parameters, we improve the fluency and readability of automatically generated text summaries.