Can anyone guide me on how to use pre-trained AI models with Hugging Face for text summarization?

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Handling long-form legal text is a classic challenge. To solve the token limit, the most common strategy is the "map-reduce" approach: chunk the document into segments that fit the model's window, summarize each, and then summarize the summaries. For the model, I highly recommend facebook/bart-large-cnn or google/pegasus-xsum. Pegasus was specifically trained for abstractive summarization and tends to produce more human-like summaries. Also, ensure you use the BigBird or Longformer architectures if you want to process up to 4096 tokens in one go.

   Answered 2025-11-20 by Michelle Young

How are you currently handling the extraction of text from those 50-page PDFs? Are you using a library like PyMuPDF or something more advanced to maintain the structural integrity of the legal clauses?

   Answered 2025-11-25 by Kevin Wright

• Kevin, we are using OCR for the older scanned documents and standard parsers for the digital ones. The structure is definitely an issue because if we cut a clause in half during chunking, the model loses the context. I’m trying to find a way to chunk by paragraph or section instead of just token counts.

     Commented 2025-12-02 by Karen Scott

You should look into the LangChain integration with Hugging Face. It has built-in recursive character text splitters that make this exact process much easier.

   Answered 2025-12-05 by Sandra King

• Sandra's suggestion is excellent. LangChain + Hugging Face is the industry standard now for RAG and long-doc summarization. It handles the "chunking" logic so you can focus on the model performance.

     Commented 2025-03-28 by Michelle Young