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

The Nature of Novelty Detection

Sentence level novelty detection aims at reducing redundant sentences from a sentence list. In the task, sentences appearing later in the list with no new meanings are eliminated. Aiming at a better accuracy for detecting redundancy, this paper reveals the nature of the novelty detection task currently overlooked by the Novelty community $-$ Novelty as a combination of the partial overlap (PO, two sentences sharing common facts) and complete overlap (CO, the first sentence covers all the facts of the second sentence) relations. By formalizing novelty detection as a combination of the two relations between sentences, new viewpoints toward techniques dealing with Novelty are proposed. Among the methods discussed, the similarity, overlap, pool and language modeling approaches are commonly used. Furthermore, a novel approach, selected pool method is provided, which is immediate following the nature of the task. Experimental results obtained on all the three currently available novelty datasets showed that selected pool is significantly better or no worse than the current methods. Knowledge about the nature of the task also affects the evaluation methodologies. We propose new evaluation measures for Novelty according to the nature of the task, as well as possible directions for future study.Comment: This paper pointed out the future direction for novelty detection research. 37 pages, double spaced versio

Semanttisten luokkien soveltaminen automaattisessa uutisseurannassa

Topic detection and tracking (TDT) is an area of information retrieval research the focus of which revolves around news events. The problems TDT deals with relate to segmenting news text into cohesive stories, detecting something new, previously unreported, tracking the development of a previously reported event, and grouping together news that discuss the same event. The performance of the traditional information retrieval techniques based on full-text similarity has remained inadequate for online production systems. It has been difficult to make the distinction between same and similar events. In this work, we explore ways of representing and comparing news documents in order to detect new events and track their development. First, however, we put forward a conceptual analysis of the notions of topic and event. The purpose is to clarify the terminology and align it with the process of news-making and the tradition of story-telling. Second, we present a framework for document similarity that is based on semantic classes, i.e., groups of words with similar meaning. We adopt people, organizations, and locations as semantic classes in addition to general terms. As each semantic class can be assigned its own similarity measure, document similarity can make use of ontologies, e.g., geographical taxonomies. The documents are compared class-wise, and the outcome is a weighted combination of class-wise similarities. Third, we incorporate temporal information into document similarity. We formalize the natural language temporal expressions occurring in the text, and use them to anchor the rest of the terms onto the time-line. Upon comparing documents for event-based similarity, we look not only at matching terms, but also how near their anchors are on the time-line. Fourth, we experiment with an adaptive variant of the semantic class similarity system. The news reflect changes in the real world, and in order to keep up, the system has to change its behavior based on the contents of the news stream. We put forward two strategies for rebuilding the topic representations and report experiment results. We run experiments with three annotated TDT corpora. The use of semantic classes increased the effectiveness of topic tracking by 10-30\% depending on the experimental setup. The gain in spotting new events remained lower, around 3-4\%. The anchoring the text to a time-line based on the temporal expressions gave a further 10\% increase the effectiveness of topic tracking. The gains in detecting new events, again, remained smaller. The adaptive systems did not improve the tracking results.Automaattinen uutistapahtumien seuranta on tietojenkäsittelytieteen ja siinä tiedonhaun piiriin kuuluva tutkimusalue, jossa kehitetään menetelmiä digitaalisen uutisvirran hallintaan. Uutisvirta koostuu useista, mahdollisesti eri kielisistä uutislähteistä, joissa voi olla digitaalisia online-uutisia ja radio- sekä televisiouutisia. Alueen tutkimusongelmat koostuvat uusien, aikaisemmin uutisoimattomien uutistapahtumien havaitsemisesta, tunnistettujen uutistapahtumien kehityksen seuraamisesta ja uutisten ryhmittelystä sisällön perusteella sekä uutisvirran pilkkomisesta uutisjutuiksi. Tässä työssä keskitytään kahteen ensimmäiseen tutkimusongelmaan. Perinteiset tiedonhakumenetelmät, jotka ovat edelleen internet-tiedonhakujärjestelmien perustana, vertailevat tekstidokumentteja joukkoina sanoja ja käsittelevät sanoja yksinkertaisina merkkijonoja, mikä mahdollistaa nopeat hakuajat ja kohtuullisen hyvä tulokset mutta kadottaa sanojen merkitykset. Perinteiset menetelmät eivät ole kuitenkaan toimineet erityisen hyvin tapahtumapohjaisessa uutisseurannassa. Erityisen vaikeaa on ollut tunnistaa kaksi samantyyppistä uutistapahtumaa, esim. kaksi lento-onnettomuutta, eri tapahtumiksi, koska niiden uutisointi sisältää pitkälti samoja sanoja. Tässä työssä etsitään uusia tapoja kuvata ja vertailla uutisia. Ensinnäkin sanat ryhmitellään merkitystensä mukaan joukoiksi samankaltaisia sanoja eli semanttisiksi luokiksi. Työssä käytetään semanttisia luokkia kuten yleiset sanat, organisaatiot, henkilöt, paikanilmaukset ja ajanilmaukset, jotka karkeasti ottaen vastaavat kysymyksiin mitä, kuka, milloin ja missä. Jokaisen luokan sisällä sanoja voidaan vertailla hieman eri tavoin, ja niinpä paikanilmausten kohdalla voidaan kaksi eri kaupunkia tai maata huomata maantieteellisesti läheisiksi tai organisaatioiden nimien kohdalla tunnistaa kaksi nimeä viittaavan samaan organisaatioon. Semanttisen luokan taustalle voidaan kytkeä sanojen taksonomia tai jokin muu rakenne, jonka kautta voidaan selvittää luokan sanojen välinen suhde. Lisäksi tekstistä tunnistetaan ajanilmaukset (esim. 'eilen', 'kaksi vuotta sitten helmikuussa') ja teksti ankkuroidaan niiden avulla aika-akselille. Tällöin tunnistetaan eri uutistapahtumista puhuttaessa samaa sanaa, esim. 'lento-onnettomuus', käytetään eri aikayhteydessä. Uutisia verrataan semanttinen luokka kerrallaan, ja tunnistaminen nojaa näiden erilaisten luokkakohtaisten tulosten yhdistelmään. Näin kaksi lento-onnettomuusuutista voivat olla samanlaisia yleisten sanojen suhteen mutta erilaisia paikkojen ja ajanilmausten suhteen, koska ne tapahtuvat eri paikoissa eri aikaan. Uutistapahtumia on monenlaisia, eikä todellisuus tai siitä kertovat uutiset taivu täysin kauniisiin malleihin. Tutkimustuloksissa kuitenkin semanttisten luokkien käyttö parantaa tuntuvasti uutistapahtumien seurannan tarkkuutta verrattuna perinteiseen lähestymistapaan -- uusien tapahtumien tunnistamista hieman vähemmän

SOTXTSTREAM: Density-based self-organizing clustering of text streams

A streaming data clustering algorithm is presented building upon the density-based selforganizing stream clustering algorithm SOSTREAM. Many density-based clustering algorithms are limited by their inability to identify clusters with heterogeneous density. SOSTREAM addresses this limitation through the use of local (nearest neighbor-based) density determinations. Additionally, many stream clustering algorithms use a two-phase clustering approach. In the first phase, a micro-clustering solution is maintained online, while in the second phase, the micro-clustering solution is clustered offline to produce a macro solution. By performing self-organization techniques on micro-clusters in the online phase, SOSTREAM is able to maintain a macro clustering solution in a single phase. Leveraging concepts from SOSTREAM, a new density-based self-organizing text stream clustering algorithm, SOTXTSTREAM, is presented that addresses several shortcomings of SOSTREAM. Gains in clustering performance of this new algorithm are demonstrated on several real-world text stream datasets

Real-time event detection in massive streams

Grant award number EP/J020664/1New event detection, also known as first story detection (FSD), has become very popular in recent years. The task consists of finding previously unseen events from a stream of documents. Despite the apparent simplicity, FSD is very challenging and has applications anywhere where timely access to fresh information is crucial: from journalism to stock market trading, homeland security, or emergency response. With the rise of user generated content and citizen journalism we have entered an era of big and noisy data, yet traditional approaches for solving FSD are not designed to deal with this new type of data. The amount of information that is being generated today exceeds by many orders of magnitude previously available datasets, making traditional approaches obsolete for modern event detection. In this thesis, we propose a modern approach to event detection that scales to unbounded streams of text, without sacrificing accuracy. This is a crucial property that enables us to detect events from large streams like Twitter, which none of the previous approaches were able to do. One of the major problems in detecting new events is vocabulary mismatch, also known as lexical variation. This problem is characterized by different authors using different words to describe the same event, and it is inherent to human language. We show how to mitigate this problem in FSD by using paraphrases. Our approach that uses paraphrases achieves state-of-the-art results on the FSD task, while still maintaining efficiency and being able to process unbounded streams. Another important property of user generated content is the high level of noise, and Twitter is no exception. This is another problem that traditional approaches were not designed to deal with, and here we investigate different methods of reducing the amount of noise. We show that by using information from Wikipedia, it is possible to significantly reduce the amount of spurious events detected in Twitter, while maintaining a very small latency in detection. A question is often raised as to whether Twitter is at all useful, especially if one has access to a high-quality stream such as the newswire, or if it should be considered as sort of a poor man’s newswire. In our comparison of these two streams we find that Twitter contains events not present in the newswire, and that it also breaks some events sooner, showing that it is useful for event detection, even in the presence of newswire

Exploring events and distributed representations of text in multi-document summarization

In this article, we explore an event detection framework to improve multi-document summarization. Our approach is based on a two-stage single-document method that extracts a collection of key phrases, which are then used in a centrality-as-relevance passage retrieval model. We explore how to adapt this single-document method for multi-document summarization methods that are able to use event information. The event detection method is based on Fuzzy Fingerprint, which is a supervised method trained on documents with annotated event tags. To cope with the possible usage of different terms to describe the same event, we explore distributed representations of text in the form of word embeddings, which contributed to improve the summarization results. The proposed summarization methods are based on the hierarchical combination of single-document summaries. The automatic evaluation and human study performed show that these methods improve upon current state-of-the-art multi-document summarization systems on two mainstream evaluation datasets, DUC 2007 and TAC 2009. We show a relative improvement in ROUGE-1 scores of 16% for TAC 2009 and of 17% for DUC 2007.info:eu-repo/semantics/submittedVersio

Multifaceted Geotagging for Streaming News

News sources on the Web generate constant streams of information, describing the events that shape our world. In particular, geography plays a key role in the news, and understanding the geographic information present in news allows for its useful spatial browsing and retrieval. This process of understanding is called geotagging, and involves first finding in the document all textual references to geographic locations, known as toponyms, and second, assigning the correct lat/long values to each toponym, steps which are termed toponym recognition and toponym resolution, respectively. These steps are difficult due to ambiguities in natural language: some toponyms share names with non-location entities, and further, a given toponym can have many location interpretations. Removing these ambiguities is crucial for successful geotagging. To this end, geotagging methods are described which were developed for streaming news. First, a spatio-textual search engine named STEWARD, and an interactive map-based news browsing system named NewsStand are described, which feature geotaggers as central components, and served as motivating systems and experimental testbeds for developing geotagging methods. Next, a geotagging methodology is presented that follows a multifaceted approach involving a variety of techniques. First, a multifaceted toponym recognition process is described that uses both rule-based and machine learning–based methods to ensure high toponym recall. Next, various forms of toponym resolution evidence are explored. One such type of evidence is lists of toponyms, termed comma groups, whose toponyms share a common thread in their geographic properties that enables correct resolution. In addition to explicit evidence, authors take advantage of the implicit geographic knowledge of their audiences. Understanding the local places known by an audience, termed its local lexicon, affords great performance gains when geotagging articles from local newspapers, which account for the vast majority of news on the Web. Finally, considering windows of text of varying size around each toponym, termed adaptive context, allows for a tradeoff between geotagging execution speed and toponym resolution accuracy. Extensive experimental evaluations of all the above methods, using existing and two newly-created, large corpora of streaming news, show great performance gains over several competing prominent geotagging methods

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey