A Study of Realtime Summarization Metrics

Unexpected news events, such as natural disasters or other human tragedies, create a large volume of dynamic text data from official news media as well as less formal social media. Automatic real-time text summarization has become an important tool for quickly transforming this overabundance of text into clear, useful information for end-users including affected individuals, crisis responders, and interested third parties. Despite the importance of real-time summarization systems, their evaluation is not well understood as classic methods for text summarization are inappropriate for real-time and streaming conditions. The TREC 2013-2015 Temporal Summarization (TREC-TS) track was one of the first evaluation campaigns to tackle the challenges of real-time summarization evaluation, introducing new metrics, ground-truth generation methodology and dataset. In this paper, we present a study of TREC-TS track evaluation methodology, with the aim of documenting its design, analyzing its effectiveness, as well as identifying improvements and best practices for the evaluation of temporal summarization systems

Entity-Centric Stream Filtering and Ranking: Filtering and Unfilterable Documents

Cumulative Citation Recommendation (CCR) is defined as: given a stream of documents on one hand and Knowledge Base (KB) entities on the other, filter, rank and recommend citation-worthy documents. The pipeline encountered in systems that approach this problem involves four stages: filtering, classification, ranking (or scoring), and evaluation. Filtering is only an initial step that reduces the web-scale corpus into a working set of documents more manageable for the subsequent stages. Nevertheless, this step has a large impact on the recall that can be attained maximally. This study analyzes in-depth the main factors that affect recall in the filtering stage. We investigate the impact of choices for corpus cleansing, entity profile construction, entity type, document type, and relevance grade. Because failing on recall in this first step of the pipeline cannot be repaired later on, we identify and characterize the citation-worthy documents that do not pass the filtering stage by examining their contents

Entity-Centric Stream Filtering and Ranking: Filtering and Unfilterable Documents

htmlabstractCumulative Citation Recommendation (CCR) is defined as: given a stream of documents on one hand and Knowledge Base (KB) entities on the other, filter, rank and recommend citation-worthy documents. The pipeline encountered in systems that approach this problem involves four stages: filtering, classification, ranking (or scoring), and evaluation. Filtering is only an initial step that reduces the web-scale corpus into a working set of documents more manageable for the subsequent stages. Nevertheless, this step has a large impact on the recall that can be at- tained maximally. This study analyzes in-depth the main factors that affect recall in the filtering stage. We investigate the impact of choices for corpus cleansing, entity profile construction, entity type, document type, and relevance grade. Because failing on recall in this first step of the pipeline cannot be repaired later on, we identify and characterize the citation-worthy documents that do not pass the filtering stage by examining their contents

On enhancing the robustness of timeline summarization test collections

Timeline generation systems are a class of algorithms that produce a sequence of time-ordered sentences or text snippets extracted in real-time from high-volume streams of digital documents (e.g. news articles), focusing on retaining relevant and informative content for a particular information need (e.g. topic or event). These systems have a range of uses, such as producing concise overviews of events for end-users (human or artificial agents). 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. sentences) 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 evaluations 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 the timeline summarization test collections fail to generalize to new summarization systems, then we propose, evaluate and analyze new automatic solutions to this issue. In particular, using a depooling methodology over 19 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 mis-ranked increases as the effectiveness of systems held-out from the pool increases. To reduce the risk of mis-ranking systems, we also propose a range of different automatic ground truth label expansion techniques. Our results show that the proposed expansion techniques can be effective at increasing the robustness of the TREC-TS test collections, as they are able to generate large numbers missing matches with high accuracy, markedly reducing the number of mis-rankings by up to 50%

Filtering News from Document Streams: Evaluation Aspects and Modeled Stream Utility

Events like hurricanes, earthquakes, or accidents can impact a large number of people. Not only are people in the immediate vicinity of the event affected, but concerns about their well-being are shared by the local government and well-wishers across the world. The latest information about news events could be of use to government and aid agencies in order to make informed decisions on providing necessary support, security and relief. The general public avails of news updates via dedicated news feeds or broadcasts, and lately, via social media services like Facebook or Twitter. Retrieving the latest information about newsworthy events from the world-wide web is thus of importance to a large section of society. As new content on a multitude of topics is continuously being published on the web, specific event related information needs to be filtered from the resulting stream of documents. We present in this thesis, a user-centric evaluation measure for evaluating systems that filter news related information from document streams. Our proposed evaluation measure, Modeled Stream Utility (MSU), models users accessing information from a stream of sentences produced by a news update filtering system. The user model allows for simulating a large number of users with different characteristic stream browsing behavior. Through simulation, MSU estimates the utility of a system for an average user browsing a stream of sentences. Our results show that system performance is sensitive to a user population's stream browsing behavior and that existing evaluation metrics correspond to very specific types of user behavior. To evaluate systems that filter sentences from a document stream, we need a set of judged sentences. This judged set is a subset of all the sentences returned by all systems, and is typically constructed by pooling together the highest quality sentences, as determined by respective system assigned scores for each sentence. Sentences in the pool are manually assessed and the resulting set of judged sentences is then used to compute system performance metrics. In this thesis, we investigate the effect of including duplicates of judged sentences, into the judged set, on system performance evaluation. We also develop an alternative pooling methodology, that given the MSU user model, selects sentences for pooling based on the probability of a sentences being read by modeled users. Our research lays the foundation for interesting future work for utilizing user-models in different aspects of evaluation of stream filtering systems. The MSU measure enables incorporation of different user models. Furthermore, the applicability of MSU could be extended through calibration based on user behavior