Using AI To Summarize Genealogy Documents

Confronted with extensive texts—such as comprehensive local history volumes, antiquated genealogy journals, detailed family narratives, or centuries-old accounts of ancestral homelands—genealogy researchers may struggle to effectively extract essential information. Navigating hundreds of pages is both time-consuming and labor-intensive. Artificial intelligence (AI) provides a transformative solution. AI-powered summarization tools efficiently identify names, themes, and critical details, enabling scholars to focus directly on the most pertinent aspects of their research.

Understanding Summarization Methods

Abstractive Summarization

Abstractive summarization employs artificial intelligence to generate a condensed version of the original text using novel phrasing. Rather than extracting exact sentences from the source material, the model synthesizes the fundamental meaning. This method offers a more human-like and flexible approach, effectively capturing the essence of complex passages in a succinct manner. However, it may introduce minor inaccuracies or omit specific names if not properly directed.

Extractive Summarization

Extractive summarization involves selecting key sentences or phrases directly from the text. This technique ensures factual consistency with the original wording; however, the resulting summary may appear fragmented and less cohesive. For genealogists, an extractive summary remains valuable for identifying pages or sections that reference particular ancestors, locations, or events.

Hybrid Approaches

Certain AI tools integrate both abstractive and extractive methodologies—initially identifying the most significant segments and subsequently generating a more coherent, human-readable abstract from them. This hybrid approach produces high-quality, contextually rich summaries that are both accurate and concise.

Preparing Documents for Summarization

Digitizing the Source

When the document intended for summarization exists solely as a scanned image, it is imperative to convert it into machine-readable text utilizing Optical Character Recognition (OCR) tools. Preferred OCR solutions include Transkribus for handwritten materials and Adobe Acrobat for printed texts. The precision of the input text is critical, as it directly influences the accuracy of the summarization process. Additionally, evaluate the OCR software bundled with your scanner, as it may offer sufficient capabilities for this task.

For books and articles available in PDF format, leverage reputable repositories such as Google Books, Hathi Trust, and the Internet Archive to obtain high-quality digital versions.

Cleaning and Formatting

Prior to submitting text to an AI summarizer, meticulously eliminate any extraneous elements, including page numbers, headers, footers, and redundant titles. A well-structured text facilitates the AI’s ability to discern coherent sections and comprehend the contextual framework. In instances involving extensive documents, it is advisable to segment the material into chapters or distinct sections to produce more precise and focused summaries.

Providing Contextual Prompts

AI models achieve optimal performance when supplied with adequate context. Prior to requesting a summary, clearly articulate the document’s nature, its historical or temporal setting, and its significance. For example:

“This is a local history book about rural New York in the late 19th century. I am looking for information related to the Crawford family, their property holdings, and any mention of their participation in the community church.”

By adhering to these protocols, you ensure that the summarization process is both efficient and effective, yielding high-quality summaries that accurately reflect the original material’s intent and content.

Research your ancestors on MyHeritage

Techniques for Summarizing Large Documents

Chunking the Document

For lengthy files (hundreds of pages), break the text into manageable chunks—perhaps one chapter or 20-page segment at a time. Summarize each section separately, then ask the AI to create a high-level summary from these section-level summaries. This step-by-step approach can reduce information overload and maintain accuracy.

Layered Summaries:

You can create a hierarchy of summaries:

High-Level Summary: Identifies main themes, time periods, and major family names.
Section Summaries: Delve deeper into each chapter or subsection, highlighting more specific events, names, and date ranges.
Detail Extraction: Once you identify the relevant sections, ask the AI to extract specific facts: birth dates, land records, marriage references, or immigration details.

By layering your approach, you can start broad and then focus more narrowly as you identify the sections of greatest interest.

Iterative Refinement

If the initial summary feels too vague or misses key details, refine your request. Add instructions like:

“Please revise the summary to highlight any mention of the Crawford family. Focus on names, dates, and property transactions between 1850 and 1900.”

This iterative process helps guide the AI toward more relevant and accurate outputs.

Choosing the Right Tool for Summarization

Different AI platforms have varying strengths. Consider the following:

ChatGPT or Claude: Excellent general-purpose summarizers with strong language capabilities. They’re well suited for producing readable, well-structured summaries.
Genesis (Google): As Google’s tools evolve, they may integrate smoothly with your Google Drive documents, simplifying the workflow.
Perplexity AI: While primarily a research assistant, it can still provide concise overviews or guide you to key sections of text.
Microsoft 365 Copilot: If you store documents in OneDrive or SharePoint, Copilot can produce summaries directly within Word Online, streamlining your research within a familiar environment.

Remember to test several tools to find which one best matches your documents and research style. You may find that ChatGPT excels at capturing narrative histories, while Claude might better handle dense academic texts.

Providing Contextual and Historical Details

For genealogical documents, historical context is crucial. AI tools trained primarily on modern text might struggle with archaic terms, outdated place names, or old-fashioned occupations. Add context to your prompt, such as:

The time period covered by the document (e.g., “This text covers events in the late 18th century in rural Bavaria.”)
The type of document (e.g., “This is a local church history that lists parishioners and their community roles.”)
The desired focus (e.g., “Focus on mentions of the Müller family, their children’s birth records, and any notes on their migration patterns.”)

By doing so, you help the AI model zero in on relevant details and interpret historical nuances more accurately.

Verifying the Results
Cross-Referencing with the Original Text: After receiving a summary, skim through the original document to confirm key facts. Did the AI correctly identify names, dates, and places? Are there inconsistencies or obvious errors? AI is a starting point, not a final authority.
Consulting External Databases and References: If the summary mentions a location or historical event, verify it against known historical timelines, genealogical indexes, or reputable archives. Cross-referencing helps confirm that the summarized information is aligned with established historical facts.
Refine and Re-Request: If you spot errors, correct them and run another query. For instance, if the AI misidentified a surname, explain that the name is spelled differently and ask it to re-check the text. This iterative correction loop can improve the accuracy of the final summary.

Practical Example

Example 1: Summarizing a 57-Page Genealogy Book dated 1916 in PDF

Break the book’s text into sections of 20–30 pages.
Ask ChatGPT to summarize each section, focusing on names, notable events, and locations mentioned.
Compile all section summaries, then instruct the AI to create a top-level summary highlighting patterns—like the migration trends of a particular family line.
If certain family names appear, request a separate summary detailing the frequency, context, and relevant events linked to them.

The Genealogy of David Putman and His Descendants

The 57-page book The Genealogy of David Putman and His Descendants was privately published in 1916 by G.W. Putman. The following actions were taken:

The PDF document was split into three sections of somewhat equal length and labeled “Part 1,” “Part 2,” and “Part 3.”
ChatGPT version 4 was used to summarize the document.
Prompt Create a detailed summary of this document focusing on names, notable events, and locations mentioned was used for each section.
All three resulting summaries were combined into one “complete” summary.

Prompt Create a top-level summary highlighting migration trends for each family was used uploading the complete summary.

AI Generated Result

Migration Trends of the Putman Family by Generation
1. Early Settlement (1600s-1700s)

Johannes Putman/Footman (b. 1645):
Origin: Dutch heritage, likely immigrated from the Netherlands.
Settlement: Schenectady, New York (Mohawk Valley).

Event: Killed in the Schenectady Massacre of 1690, leaving children orphaned.

2. Second Generation (Late 1600s - Early 1700s)

Victor Putman (b. ~1680):
Migration: Settled near Schenectady, NY, continuing the family’s foothold in the Mohawk Valley.

Family expanded into nearby Albany County through marriage into the Mabee family.

3. Third Generation (1700s - Early 1800s)

David Putman (b. 1717 - d. 1806):
Migration: Established farms near Tribes Hill, NY, after military service in the Revolutionary War.

Children spread into central New York counties, including Montgomery, Jefferson, and Schenectady.

4. Expansion into New York Frontier (Late 1700s - Early 1800s)

David Putman’s Children:
Cornelius, Peter, Jacob, Margaret, Ahda, and Alida Putman:
Moved westward toward newly developing frontier towns in New York such as Cherry Valley, Millville, and Bonfire, NY.

5. Westward Migration (1800s-1900s)

Next Generations:
Abraham Putman: Migrated toward New York’s expanding frontier near Tribes Hill and beyond.

George W. Putman: As the family historian, he documented family branches moving into Michigan, Minnesota, and other parts of the Midwest.

6. Midwest Settlements (Mid-1800s - Early 1900s)

Minnesota Settlements:
Descendants settled in Swift County, St. Peter, and Benson, MN.

Alexander Utter and Alida Putman’s descendants: Contributed to family records from Minnesota.

Michigan Stops:
Notable settlements included Detroit and Spirit Lake, MI, marking key westward destinations.

Summary of Migration Patterns

Origin: Dutch immigration to New York (Mohawk Valley).

Core Settlements: Schenectady, Tribes Hill, and surrounding NY counties.

Expansion: Westward movement into Michigan, Minnesota, and the broader Midwest due to land opportunities and frontier expansion.

Notable Stops: Schenectady, Albany, Tribes Hill, Cherry Valley, Jefferson County (NY); Detroit (MI); St. Peter, Swift County, and Benson (MN).
The Putman family’s migration reflects a classic pattern of early Dutch settlement in New York, expansion along the Mohawk Valley, and later frontier migration driven by land availability and family growth.

Beyond Summarization: Next Steps

Once you have a summary highlighting key facts and figures, you can use it as a springboard for further research:

Targeted Translation: If the document is in another language, you now know which sections are worth translating in detail.
Deeper Analysis: Summaries can point you towards patterns—such as recurring locations or occupations—that warrant deeper investigation.
Integration with Family Tree Software: Relevant data extracted from summaries (names, birth dates, migration events) can be added to your genealogical database or family tree software for future reference.

Tips for Summarizing Genealogy Documents

Focus Your Prompt. Instead of a generic “summarize this document,” specify what you’re looking for: “Summarize key events related to the Wilson family migration from Ireland to Canada in the 1850s found in these 50 pages.”
Break Documents into Manageable Chunks. If dealing with a very large text, summarize it chapter by chapter or section by section. Then, ask the AI to create a master summary from these partial summaries.
Highlight Important Terms. Before summarizing, tell the AI which surnames, places, or events matter to you. This ensures the summary highlights relevant details rather than generic information.
Ask for Different Levels of Detail. Start with a high-level overview. If something seems interesting—such as a particular family’s migration—request a more granular summary focusing only on that topic.
Use Summaries as a Discovery Tool. Summaries can help identify which sections of a long text contain valuable genealogical details, saving you from reading irrelevant pages. Once you know where the “golden nuggets” are, you can do a closer reading or a more detailed analysis.

Set Realistic Expectations. AI tools greatly speed up certain tasks, but they are not infallible. Expect some errors, and treat AI output as a starting point.
Use AI as a Research Assistant, Not a Replacement. While these tools can handle repetitive tasks and grunt work, human expertise remains essential. Your understanding of historical context, your ability to spot inconsistencies, and your critical thinking are irreplaceable.
Practice Makes Perfect. The more you work with these tools, the better you’ll become at crafting effective prompts, fine-tuning results, and integrating AI outputs into your research workflow.

Conclusion

Utilizing artificial intelligence to summarize extensive genealogical documents is a transformative strategy. With appropriate preparation and the right tools, you can efficiently evaluate a source and determine its potential for further investigation. The key lies in providing the AI with sufficient historical context and continuously refining your prompts to achieve optimal results. As you enhance your ability to guide the AI and verify its outputs, you will find that even the most complex historical records can produce valuable insights, eliminating the traditional challenges associated with such tasks.

Explore more about using AI to summarize genealogy documents

AI & Genealogy: Harnessing the Power of Artificial Intelligence for Family History Research on the MyHeritage Knowledge base
ChatGPT
Claude
Copilot
Gemini
Perplexity