Can Haystack be used for document summarization tasks?

Yes, Haystack can be used for document summarization tasks, though it requires careful setup and isn’t its primary use case out of the box. Haystack is an open-source framework designed for building search and question-answering systems, leveraging retrieval-augmented pipelines. While its core focus is on retrieving and processing information from large document collections, its modular architecture allows developers to adapt it for summarization by combining retrieval models with text-generation components. For example, you can use Haystack’s pipelines to first extract relevant passages from documents and then pass them to a summarization model to generate concise outputs.

To implement summarization, you might start by using Haystack’s retriever components (like BM25 or dense retrievers such as DPR) to identify key sections of a document. These sections can then be fed into a generator model, such as BART or T5, fine-tuned for summarization. Haystack’s Pipeline class allows chaining these steps: a retriever fetches candidate text chunks, and a generator processes them into summaries. Developers can also customize the pipeline—for instance, by adding a node to aggregate multiple summaries or filter redundant information. This flexibility makes it possible to tailor the system for specific needs, such as summarizing legal documents or technical reports.

However, there are limitations. Haystack isn’t optimized for processing extremely long documents in a single pass, which is common in summarization tasks. You may need to split documents into smaller chunks before processing, which can affect the coherence of the final summary. Additionally, while Haystack supports integration with Hugging Face models, the quality of summaries depends heavily on the choice of the underlying generator model and its training data. Developers might need to fine-tune models on domain-specific data to improve results. Alternatives like dedicated summarization libraries (e.g., Hugging Face’s transformers pipeline) might offer more streamlined solutions for straightforward use cases, but Haystack’s strength lies in combining retrieval and generation for context-aware summarization in complex workflows.