# Explaining Documents' Relevance to Search Queries

arXiv: https://arxiv.org/abs/2111.01314 · HTML: https://mlsanity.com/paper/2111.01314/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2021.

## Built on by (most cited first, 5 of 5)

- [Explain like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation](https://mlsanity.com/paper/2304.12631/) (2023)
- [RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank](https://mlsanity.com/paper/2405.01848/) (2024)
- [ExaRanker: Explanation-Augmented Neural Ranker](https://mlsanity.com/paper/2301.10521/) (2023)
- [Probing Ranking LLMs: A Mechanistic Analysis for Information Retrieval](https://mlsanity.com/paper/2410.18527/) (2024)
- [Explainability of Text Processing and Retrieval Methods: A Survey](https://mlsanity.com/paper/2212.07126/) (2022)

## Abstract (arXiv)

We present GenEx, a generative model to explain search results to users beyond just showing matches between query and document words. Adding GenEx explanations to search results greatly impacts user satisfaction and search performance. Search engines mostly provide document titles, URLs, and snippets for each result. Existing model-agnostic explanation methods similarly focus on word matching or content-based features. However, a recent user study shows that word matching features are quite obvious to users and thus of slight value. GenEx explains a search result by providing a terse description for the query aspect covered by that result. We cast the task as a sequence transduction problem and propose a novel model based on the Transformer architecture. To represent documents with respect to the given queries and yet not generate the queries themselves as explanations, two query-attention layers and masked-query decoding are added to the Transformer architecture. The model is trained without using any human-generated explanations. Training data are instead automatically constructed to ensure a tolerable noise level and a generalizable learned model. Experimental evaluation shows that our explanation models significantly outperform the baseline models. Evaluation through user studies also demonstrates that our explanation model generates short yet useful explanations.