In this repo, we release the processed MIMICS dataset used in our SIGIR ‘22 paper Towards Explainable Search Results: A Listwise Explanation Generator. This is a processed version of MIMICS combining aspect annotation from https://github.com/microsoft/MIMICS and SERP information from http://ciir.cs.umass.edu/downloads/mimics-serp/MIMICS-BingAPI-results.zip.

All files are stored with pickle. To read files, use

import pickle

with open("xxx.pkl", 'rb') as fin:
    data = pickle.load(fin)

Descritption of each file

The main thing to note is that SERP snippets in MIMICS are query-dependent, meaning that the document from the same URL contains different content for different queries. Therefore, doc-id is named with *query*-number.

• id2doc.pkl is a {k: v} dictionary, where k is document id, and v the snippet text.
• aspect2documents.pkl is a {k: a: set} nested dictionary, where k is query, a an aspect, and set a set of document ids relevant to that aspect.
• train_queries.pkl and test_queries.pkl are two sets, each containing a around 1K queries. This is the train/test split used in our paper.
• serps.pkl is a {k: list} dictionary, where k is query, and list the list of document ids retrieved by Bing for the query.

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