Many organizations rely heavily on SharePoint search to find documents, but often that experience falls short. Native SharePoint search results typically show titles and short, query-influenced snippets that often lack enough context to assess relevance. These snippets can be too vague to gauge relevance, forcing users to open documents and search for the query terms themselves. In contrast, query-aware summarization generates a concise, query-focused snippet for each result. Instead of a sentence fragment that may or may not be relevant, each summary explains how the document addresses your specific query. This also differs from the generic summaries that some AI-based tools may show you.
This blog explores why both Generic LLM-generated snippets often fail because they are decoupled from the user’s specific query intent and the retrieval context of the individual document. It will discuss how PointFire overcomes this by linking its query-aware summarization to AI-enhanced query processing. By translating natural language into optimized KQL-style queries before retrieval, PointFire provides the summarizer with a deeper understanding of what the user is looking for, ensuring the resulting snippet highlights the most relevant information.
Why traditional search snippets fall short
In SharePoint search, search snippets, also called “Hit Highlighted Summaries”, are not summaries at all, they are a part of a sentence plucked from within a document which an algorithm has selected as having relevance. It is rarely relevant to the search, but might contain some matching words.
Why static summaries fall short
In SharePoint search, static AI-generated summaries often provide limited context. PointFire notes that “most AI tools generate document-centric summaries that are reused across different queries, rather than adapting the summary to each user’s specific search intent”. A static summary might highlight the beginning of a 50-page report even if the query term appears later, forcing you to open each document. By contrast, query-aware summarization creates a synopsis of only the parts relevant to your query. In real-world use, a query-aware summary could clearly point out that the subject you searched appears in specific parts of the document, such as sections three and seven, or it might transparently indicate that no content in the file matches your search at all.
The power of query-aware summarization
Query-aware summarization uses advanced AI techniques. PointFire explains that it combines query-focused logic with extractive and abstractive methods; thereby providing a cohesive summary while allowing users to hover over any result to see the exact original sentences in context, ensuring a high level of transparency… providing a factual audit trail that significantly reduces the risk of AI hallucinations. The result is a snippet generated from the document content, explicitly conditioned on your specific query. For example, the system features cross-language compatibility, meaning a user can pose a query in French to search through a repository of French documents, and see a summary generated in their preferred UI language (like Dutch), while maintaining a high degree of semantic accuracy across languages. It also highlights the top relevant sentences from each result in context, so you can see at a glance how your keywords are addressed. Importantly, all AI processing occurs within your organizational security boundary, ensuring that only the content the user can access is summarized and that data never leaves your controlled environment, so only the content that the user can access is summarized. The Summarizer is built as an extension to the popular PnP Modern Search, integrating seamlessly with SharePoint’s interface.
PointFire’s query-focused search solution
PointFire’s Search Summarizer puts these ideas into practice. Each search result is augmented with a clear summary explaining why it matches your query. Administrators can configure the summary length and content filters. The tool also shows the most relevant sentences from each document in context, so you can verify the match without opening the file. Because of the way it processes documents, it handles even very long files while preserving key information relevant to the query. All processing respects SharePoint permissions and runs in your own environment, ensuring data privacy. The tool offers automatic or on-demand summaries, allowing organizations to provide instant insights while controlling API costs by only generating summaries when a user specifically requests them.
In summary, query-aware summarization makes SharePoint search far more useful. Instead of generic snippets, each result gets a customized summary that highlights its relevance. PointFire’s approach shows exactly what matters in each document, saving users time and reducing frustration. In a complex SharePoint environment, this science-based strategy ensures that every query returns meaningful, context-rich insights.
