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Retrieving relevant plots from the book for a query is a critical task, whichcan improve the reading experience and efficiency of readers. Readers usuallyonly give an abstract and vague description as the query based on their ownunderstanding, summaries, or speculations of the plot, which requires theretrieval model to have a strong ability to estimate the abstract semanticassociations between the query and candidate plots. However, existinginformation retrieval (IR) datasets cannot reflect this ability well. In thispaper, we propose Plot Retrieval, a labeled dataset to train and evaluate theperformance of IR models on the novel task Plot Retrieval. Text pairs in PlotRetrieval have less word overlap and more abstract semantic association, whichcan reflect the ability of the IR models to estimate the abstract semanticassociation, rather than just traditional lexical or semantic matching.Extensive experiments across various lexical retrieval, sparse retrieval, denseretrieval, and cross-encoder methods compared with human studies on PlotRetrieval show current IR models still struggle in capturing abstract semanticassociation between texts. Plot Retrieval can be the benchmark for furtherresearch on the semantic association modeling ability of IR models.

Plot Retrieval as an Assessment of Abstract Semantic Association