Automatic Ground Truth Expansion for Timeline Evaluation

Mccreadie, R., Macdonald, C. and Ounis, I. (2018) Automatic Ground Truth Expansion for Timeline Evaluation. In: 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, USA, 8-12 Jul 2018, pp. 685-694. ISBN 9781450356572 (doi:10.1145/3209978.3210034)

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Abstract

The development of automatic systems that can produce timeline summaries by filtering high-volume streams of text documents, retaining only those that are relevant to a particular information need (e.g. topic or event), remains a very challenging task. To advance the field of automatic timeline generation, robust and reproducible evaluation methodologies are needed. To this end, several evaluation metrics and labeling methodologies have recently been developed - focusing on information nugget or cluster-based ground truth representations, respectively. These methodologies rely on human assessors manually mapping timeline items (e.g. tweets) to an explicit representation of what information a 'good' summary should contain. However, while these evaluation methodologies produce reusable ground truth labels, prior works have reported cases where such labels fail to accurately estimate the performance of new timeline generation systems due to label incompleteness. In this paper, we first quantify the extent to which timeline summary ground truth labels fail to generalize to new summarization systems, then we propose and evaluate new automatic solutions to this issue. In particular, using a depooling methodology over 21 systems and across three high-volume datasets, we quantify the degree of system ranking error caused by excluding those systems when labeling. We show that when considering lower-effectiveness systems, the test collections are robust (the likelihood of systems being miss-ranked is low). However, we show that the risk of systems being miss-ranked increases as the effectiveness of systems held-out from the pool increases. To reduce the risk of miss-ranking systems, we also propose two different automatic ground truth label expansion techniques. Our results show that our proposed expansion techniques can be effective for increasing the robustness of the TREC-TS test collections, markedly reducing the number of miss-rankings by up to 50% on average among the scenarios tested.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Macdonald, Dr Craig and Ounis, Professor Iadh and Mccreadie, Mr Richard
Authors: Mccreadie, R., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781450356572
Copyright Holders:Copyright © 2018 Association for Computing Machinery
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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