CPS-MEBR: Click Feedback-Aware Web Page Summarization for Multi-Embedding-Based Retrieval
Wenbiao Li, Pan Tang, Zhengfan Wu, Weixue Lu, Minghua Zhang, Zhenlei, Tian, Daiting Shi, Yu Sun, Simiu Gu, Dawei Yin

TL;DR
This paper introduces CPS-MEBR, a web page summarization framework that generates multiple embeddings based on user click data to improve retrieval accuracy for complex web pages.
Contribution
It proposes a novel multi-embedding retrieval model trained on click data, enhancing web page representation for better search relevance.
Findings
Outperforms single-embedding retrieval in candidate quality
Uses click data to identify key sentences for summarization
Employs sentence-level semantic interaction for multiple embeddings
Abstract
Embedding-based retrieval (EBR) is a technique to use embeddings to represent query and document, and then convert the retrieval problem into a nearest neighbor search problem in the embedding space. Some previous works have mainly focused on representing the web page with a single embedding, but in real web search scenarios, it is difficult to represent all the information of a long and complex structured web page as a single embedding. To address this issue, we design a click feedback-aware web page summarization for multi-embedding-based retrieval (CPS-MEBR) framework which is able to generate multiple embeddings for web pages to match different potential queries. Specifically, we use the click data of users in search logs to train a summary model to extract those sentences in web pages that are frequently clicked by users, which are more likely to answer those potential queries.…
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Taxonomy
TopicsTopic Modeling · Text and Document Classification Technologies · Multimodal Machine Learning Applications
