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Generative retrieval (Wang et al., 2022; Tay et al., 2022) is a popularapproach for end-to-end document retrieval that directly generates documentidentifiers given an input query. We introduce summarization-based documentIDs, in which each document's ID is composed of an extractive summary orabstractive keyphrases generated by a language model, rather than an integer IDsequence or bags of n-grams as proposed in past work. We find that abstractive,content-based IDs (ACID) and an ID based on the first 30 tokens are veryeffective in direct comparisons with previous approaches to ID creation. Weshow that using ACID improves top-10 and top-20 recall by 15.6% and 14.4%(relative) respectively versus the cluster-based integer ID baseline on theMSMARCO 100k retrieval task, and 9.8% and 9.9% respectively on theWikipedia-based NQ 100k retrieval task. Our results demonstrate theeffectiveness of human-readable, natural-language IDs created throughsummarization for generative retrieval. We also observed that extractive IDsoutperformed abstractive IDs on Wikipedia articles in NQ but not the snippetsin MSMARCO, which suggests that document characteristics affect generativeretrieval performance.

Summarization-Based Document IDs for Generative Retrieval with Language Models