INEX 2011 Snippet Retrieval Track


Final results available.


The goal of the snippet retrieval track is to determine how best to generate informative snippets for search results. Such snippets should provide sufficient information to allow the user to determine the relevance of each document, without needing to view the document itself. Participating organisations will compare both the effectiveness of their focused retrieval systems as well as the effectiveness of their snippet generation systems, to others.


The snippet retrieval track will use the INEX Wikipedia collection. Topics will be recycled from previous ad hoc tracks.


Participating organisations will submit a ranked list of documents, and corresponding snippets. For those organisations uninterested in developing their own focused retrieval system, a reference run will be provided, consisting of a ranked list of documents, for which snippets should be created.
Each run will be submitted in the form of an XML file (format described below). Each submission should contain 500 snippets per topic, with a maximum of 300 characters per snippet. The snippets themselves may be created in any way you wish – they may consist of summaries, passages from the document, or any other text at all.
Participants may submit more than one submission; however submissions must be ranked in order of importance, as not all submissions may be evaluated.

Submission format

The DTD for the submission format is as follows.

<!ELEMENT inex-snippet-submission (description,topic+)>
<!ATTLIST inex-snippet-submission
  participant-id CDATA #REQUIRED
<!ELEMENT description (#PCDATA)>
<!ELEMENT topic (snippet+)>
<!ATTLIST topic
  topic-id CDATA #REQUIRED
<!ELEMENT snippet (#PCDATA)>
<!ATTLIST snippet
Each submission must contain the following: Every run should contain the results for each topic, conforming to the following: An example submission in the correct format is given below.

<?xml version="1.0"?>
<!DOCTYPE inex-snippet-submission SYSTEM "inex-snippet-submission.dtd">
<inex-snippet-submission participant-id="20" run-id="Qut_Snippet_Run_01">
    <description>A description of the approach used.</description>
    <topic topic-id="2011001">
        <snippet doc-id="16080300" rsv="0.9999">...</snippet>
        <snippet doc-id="16371300" rsv="0.9998">...</snippet>
    <topic topic-id="2011002">
        <snippet doc-id="1686300" rsv="0.9999">...</snippet>
        <snippet doc-id="1751300" rsv="0.9997">...</snippet>

Relevance assessments

Documents for each topic will be manually assessed for relevance based on the snippets alone, as the goal is to determine the effectiveness of the snippet’s ability to provide sufficient information about the document behind the snippet. Each topic within a submission will be assigned an assessor, who will assess each document within the ranked list, based on the snippet alone, until a predetermined number of relevant documents are found, or until there are no documents remaining. The assignment of assessors to topics will be shuffled for each submission to avoid bias introduced by assessors judging the same topic twice, and to allow each submission to be judged by multiple assessors.

It is hoped that crowd-sourcing may be used for assessment; however participants should also expect to perform a number of assessments, depending on the number of runs they submit. As the assessment is based on short snippets rather than whole documents, the assessment load will be relatively light.

Download the assessment tool (with instructions)


The primary evaluation metric, and the one which determines the ranking, is the geometric mean of recall and negative recall (GM), averaged over all topics: sqrt(TN/(TN+FP) * TP/(TP+FN)).

Other metrics include:



Shlomo Geva

Andrew Trotman
University of Otago

Falk Scholer

Mark Sanderson

Matthew Trappett
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