Semantic snippet construction for search engine results based on segment evaluation

TL;DR

This paper introduces a semantic segment evaluation model to construct more effective search engine snippets, improving user click decisions by providing semantically relevant summaries based on multiple evaluation factors.

Contribution

It presents a novel semantic evaluation model for segments in web pages to construct better search snippets, validated through a prototype implementation.

Findings

The model effectively identifies key segments for snippets.

Semantic snippets improve user click accuracy.

Prototype confirms the model's practical viability.

Abstract

The result listing from search engines includes a link and a snippet from the web page for each result item. The snippet in the result listing plays a vital role in assisting the user to click on it. This paper proposes a novel approach to construct the snippets based on a semantic evaluation of the segments in the page. The target segment(s) is/are identified by applying a model to evaluate segments present in the page and selecting the segments with top scores. The proposed model makes the user judgment to click on a result item easier since the snippet is constructed semantically after a critical evaluation based on multiple factors. A prototype implementation of the proposed model confirms the empirical validation.