There is growing interest in systems that generate timeline summaries by filtering high-volume streams of documents to retain only those that are relevant to a particular event or topic. Continued advances in algorithms and techniques for this task depend on standardized and reproducible evaluation methodologies for comparing systems. However, timeline summary evaluation is still in its infancy, with competing methodologies currently being explored in international evaluation forums such as TREC. One area of active exploration is how to explicitly represent the units of information that should appear in a "good" summary. Currently, there are two main approaches, one based on identifying nuggets in an external "ground truth", and the other based on clustering system outputs. In this paper, by building test collections that have both nugget and cluster annotations, we are able to compare these two approaches. Specifically, we address questions related to evaluation effort, differences in the final evaluation products, and correlations between scores and rankings generated by both approaches. We summarize advantages and disadvantages of nuggets and clusters to offer recommendations for future system evaluations.

Information retrieval test collections are typically built using data from large-scale evaluations in international forums such as TREC, CLEF, and NTCIR. Previous validation studies on pool-based test collections for ad hoc retrieval have examined their reusability to accurately assess the effectiveness of systems that did not participate in the original evaluation. To our knowledge, the reusability of test collections derived from "living labs" evaluations, based on logs of user activity, has not been explored. In this paper, we performed a "leave-one-out" analysis of human judgment data derived from the TREC 2016 Real-Time Summarization Track and show that those judgments do not appear to be reusable. While this finding is limited to one specific evaluation, it does call into question the reusability of test collections built from living labs in general, and at the very least suggests the need for additional work in validating such experimental instruments.

Nugget-based evaluations, such as those deployed in the TREC Temporal Summarization and Question Answering tracks, require human assessors to determine whether a nugget is present in a given piece of text. This process, known as nugget annotation, is labor-intensive. In this paper, we present two active learning techniques that prioritize the sequence in which candidate nugget/sentence pairs are presented to an assessor, based on the likelihood that the sentence contains a nugget. Our approach builds on the recognition that nugget annotation is similar to high-recall retrieval, and we adapt proven existing solutions. Simulation experiments with four existing TREC test collections show that our techniques yield far more matches for a given level of effort than baselines that are typically deployed in previous nugget-based evaluations.

People track news events according to their interests and available time. For a major event of great personal interest, they might check for updates several times an hour, taking time to keep abreast of all aspects of the evolving event. For minor events of more marginal interest, they might check back once or twice a day for a few minutes to learn about the most significant developments. Systems generating streams of updates about evolving events can improve user performance by appropriately filtering these updates, making it easy for users to track events in a timely manner without undue information overload. Unfortunately, predicting user performance on these systems poses a significant challenge. Standard evaluation methodology, designed for Web search and other adhoc retrieval tasks, adapts poorly to this context. In this paper, we develop a simple model that simulates users checking the system from time to time to read updates. For each simulated user, we generate a trace of their activities alternating between away times and reading times. These traces are then applied to measure system effectiveness. We test our model using data from the TREC 2013 Temporal Summarization Track (TST) comparing it to the effectiveness measures used in that track. The primary TST measure corresponds most closely with a modeled user that checks back once a day on average for an average of one minute. Users checking more frequently for longer times may view the relative performance of participating systems quite differently. In light of this sensitivity to user behavior, we recommend that future experiments be built around clearly stated assumptions regarding user interfaces and access patterns, with effectiveness measures reflecting these assumptions.

We examine the effects of expanding a judged set of sentences with their duplicates from a corpus. Including new sentences that are exact duplicates of the previously judged sentences may allow for better estimation of performance metrics and enhance the reusability of a test collection. We perform experiments in context of the Temporal Summarization Track at TREC 2013. We find that adding duplicate sentences to the judged set does not significantly affect relative system performance. However, we do find statistically significant changes in the performance of nearly half the systems that participated in the Track. We recommend adding exact duplicate sentences to the set of relevance judgements in order to obtain a more accurate estimate of system performance.