Prompt architectures for document summarization pipelines

Document summarization involves condensing long documents into shorter summaries that retain the essential information. This task can be approached using different techniques, especially when dealing with large-scale pipelines. Below are several prompt architectures that could enhance document summarization in a pipeline:

1. Extractive Summarization Prompts

In extractive summarization, the model selects portions of the input document (e.g., sentences or paragraphs) and stitches them together to form a concise summary.

Prompt Architecture:

• Objective: Extract relevant sentences or passages.

• Input Format: “Given the following document, identify the most important sentences that summarize the key points.”

• Prompt Example:

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  "Here is the document you need to summarize: {DOCUMENT_CONTENT}.
   Select the key sentences that best represent the main ideas of the document."
  

This type of prompt works well with transformer-based models like BERT or RoBERTa, which are fine-tuned for extractive tasks.

Variation:

If a pipeline is built to return a fixed number of sentences, the prompt can be adjusted as follows:

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"Here is the document you need to summarize: {DOCUMENT_CONTENT}.
Select the top 5 most relevant sentences that summarize the document."

2. Abstractive Summarization Prompts

Abstractive summarization generates new text that paraphrases the document’s main ideas rather than directly extracting sentences.

Prompt Architecture:

• Objective: Create a summary by paraphrasing the document.

• Input Format: “Summarize the following document in your own words.”

• Prompt Example:

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  "Here is the document to summarize: {DOCUMENT_CONTENT}.
   Please provide a concise summary of the document in your own words."
  

This prompt is suitable for models like GPT-3, T5, or BART, which are effective in generating coherent summaries from scratch.

Variation:

If you need a summary with a specific length (e.g., a one-sentence summary):

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"Here is the document: {DOCUMENT_CONTENT}.
Please provide a one-sentence summary."

3. Topic-Based Summarization Prompts

This type of summarization focuses on providing summaries by highlighting key topics or sections.

Prompt Architecture:

• Objective: Generate a summary that includes key topics or aspects of the document.

• Input Format: “What are the main topics or sections of the following document?”

• Prompt Example:

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  "Here is the document: {DOCUMENT_CONTENT}.
   What are the primary topics covered in this document? List them and provide a brief summary of each."
  

This type of prompt is useful when you need the model to focus on topic-level summarization rather than document-level summarization.

4. Hierarchical Summarization Prompts

For complex documents, hierarchical summarization involves generating summaries at different levels (e.g., paragraph-level, section-level, document-level).

Prompt Architecture:

• Objective: Generate summaries at multiple levels of granularity.

• Input Format: “Summarize each section of the document and then summarize the overall document.”

• Prompt Example:

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  "Here is the document: {DOCUMENT_CONTENT}.
   First, summarize each section of the document.
   Then, provide a final summary of the entire document."
  

This architecture helps break down the summarization process into manageable steps, making it suitable for lengthy documents like research papers or legal contracts.

5. Sentiment-Aware Summarization Prompts

This type of summarization goes beyond content and structure, focusing on the sentiment expressed in the document.

Prompt Architecture:

• Objective: Generate summaries with sentiment analysis.

• Input Format: “Provide a summary of the document and indicate its sentiment.”

• Prompt Example:

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  "Here is the document: {DOCUMENT_CONTENT}.
   Summarize the document and identify its overall sentiment (positive, negative, or neutral)."
  

This can be useful for analyzing customer feedback, reviews, or social media content.

6. Keyword-Driven Summarization Prompts

This approach can be used when summarization needs to focus on certain keywords or themes.

Prompt Architecture:

• Objective: Generate a summary based on specific keywords.

• Input Format: “Create a summary of the document focusing on the keywords: {KEYWORDS}.”

• Prompt Example:

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  "Here is the document: {DOCUMENT_CONTENT}.
   Please summarize the document with a focus on the following keywords: {KEYWORDS}."
  

7. Summary with References to Source Document

For legal, academic, or technical content, users might want a summary that refers to specific sections or citations from the source document.

Prompt Architecture:

• Objective: Create a summary that retains references to sections, figures, or citations.

• Input Format: “Provide a summary of the document while maintaining references to figures, tables, or sections.”

• Prompt Example:

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  "Here is the document: {DOCUMENT_CONTENT}.
   Summarize the content while keeping references to important sections, figures, and tables intact."
  

This architecture is beneficial when the reader may want to track back to original sources.

8. Query-Based Summarization Prompts

If the document is large and covers multiple subtopics, it might be useful to provide a query-based prompt where the model responds to specific questions rather than generating a full summary.

Prompt Architecture:

• Objective: Generate a summary based on user queries.

• Input Format: “Given the document, answer the following question: {QUERY}”

• Prompt Example:

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  "Here is the document: {DOCUMENT_CONTENT}.
   Based on this document, answer the following question: {QUERY}"
  

This approach can narrow down the summary to only the relevant portions that answer specific questions, rather than summarizing the entire document.

9. Multi-Document Summarization Prompts

In cases where you have multiple related documents, the summarization pipeline can be used to merge and summarize them cohesively.

Prompt Architecture:

• Objective: Provide a combined summary of multiple documents.

• Input Format: “Summarize the key ideas from the following documents.”

• Prompt Example:

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  "Here are the documents: {DOCUMENT_1_CONTENT}, {DOCUMENT_2_CONTENT}, {DOCUMENT_3_CONTENT}.
   Provide a summary that combines the key points from all three documents."
  

This works well for aggregating information from multiple sources on the same topic.

Conclusion

Choosing the right prompt architecture depends largely on the type of summarization required (extractive vs. abstractive, single document vs. multiple documents, etc.) and the specific needs of the user, such as focusing on sentiment, topics, or section-level detail. As summarization tasks can vary in complexity, these prompts can be combined into a more sophisticated pipeline where different models or approaches can be applied at various stages.