Towards Explainable Search Results: A Listwise Explanation Generator

It has been shown that the interpretability of search results is enhanced when query aspects covered by documents are explicitly provided. However, existing work on aspect-oriented explanation of search results explains each document independently. These explanations thus cannot describe the differences between documents. This issue is also true for existing models on query aspect generation. Furthermore, these models provide a single query aspect for each document, even though documents often cover multiple query aspects. To overcome these limitations, we propose LiEGe, an approach that jointly explains all documents in a search result list. LiEGe provides semantic representations at two levels of granularity -- documents and their tokens -- using different interaction signals including cross-document interactions. These allow listwise modeling of a search result list as well as the generation of coherent explanations for documents. To appropriately explain documents that cover multiple query aspects, we introduce two settings for search result explanation: comprehensive and novelty explanation generation. LiEGe is trained and evaluated for both settings. We evaluate LiEGe on datasets built from Wikipedia and real query logs of the Bing search engine. Our experimental results demonstrate that LiEGe outperforms all baselines, with improvements that are substantial and statistically significant.