Time Aware Knowledge Extraction for Microblog Summarization on Twitter

Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.Comment: 33 pages, 10 figure

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

Improving search effectiveness in sentence retrieval and novelty detection

In this thesis we study thoroughly sentence retrieval and novelty detec- tion. We analyze the strengths and weaknesses of current state of the art methods and, subsequently, new mechanisms to address sentence retrieval and novelty detection are proposed. Retrieval and novelty detection are related tasks: usually, we initially apply a retrieval model that estimates properly the relevance of passages (e.g. sentences) and generates a ranking of passages sorted by their relevance. Next, this ranking is used as the input of a novelty detection module, which tries to filter out redundant passages in the ranking. The estimation of relevance at sentence level is di cult. Standard meth- ods used to estimate relevance are simply based on matching query and sentence terms. However, queries usually contain two or three terms and sentences are also short. Therefore, the matching between query and sen- tences is poor. In order to address this problem, we study how to enrich this process with additional information: the context. The context refers to the information provided by the surrounding sentences or the document where the sentence is located. Such context reduces ambiguity and supplies additional information not included in the sentence itself. Additionally, it is important to estimate how important (central) a sentence is within the docu- ment. These two components are studied following a formal framework based on Statistical Language Models. In this respect, we demonstrate that these components yield to improvements in current sentence retrieval methods. In this thesis we work with collections of sentences that were extracted from news. News not only explain facts but also express opinions that people have about a particular event or topic. Therefore, the proper estimation of which passages are opinionated may help to further improve the estimation of relevance for sentences. We apply a formal methodology that helps us to incorporate opinions into standard sentence retrieval methods. Additionally, we propose simple empirical alternatives to incorporate query-independent features into sentence retrieval models. We demonstrate that the incorpo- ration of opinions to estimate relevance is an important factor that makes sentence retrieval methods more effective. Along this study, we also analyze query-independent features based on sentence length and named entities. The combination of the context-based approach with the incorporation of opinion-based features is straightforward. We study how to combine these two approaches and its impact. We demonstrate that context-based models are implicitly promoting sentences with opinions and, therefore, opinion- based features do not help to further improve context-based methods. The second part of this thesis is dedicated to novelty detection at sentence level. Because novelty is actually dependent on a retrieval ranking, we con- sider here two approaches: a) the perfect-relevance approach, which consists of using a ranking where all sentences are relevant; and b) the non-perfect rel- evance approach, which consists of applying first a sentence retrieval method. We rst study which baseline performs the best and, next, we propose a number of variations. One of the mechanisms proposed is based on vocab- ulary pruning. We demonstrate that considering terms from the top ranked sentences in the original ranking helps to guide the estimation of novelty. The application of Language Models to support novelty detection is another chal- lenge that we face in this thesis. We apply di erent smoothing methods in the context of alternative mechanisms to detect novelty. Additionally, we test a mechanism based on mixture models that uses the Expectation-Maximization algorithm to obtain automatically the novelty score of a sentence. In the last part of this work we demonstrate that most novelty methods lead to a strong re-ordering of the initial ranking. However, we show that the top ranked sentences in the initial list are usually novel and re-ordering them is often harmful. Therefore, we propose di erent mechanisms that determine the position threshold where novelty detection should be initiated. In this respect, we consider query-independent and query-dependent approaches. Summing up, we identify important limitations of current sentence re- trieval and novelty methods, and propose novel and effective methods