Website summarization: a topic hierarchy based approach.

Liu Nan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 84-88).Abstracts in English and Chinese.Abstract --- p.1Acknowledgements --- p.3Contents --- p.4List of Figures --- p.6List of Tables --- p.7Chapter Chapter 1 --- Introduction --- p.8Chapter Chapter 2 --- Related Work --- p.12Chapter 2.1 --- Web Structure Mining --- p.12Chapter 2.1.1 --- HITS Algorithm --- p.13Chapter 2.1.2 --- PageRank Algorithm --- p.13Chapter 2.2 --- Website Mining --- p.14Chapter 2.2.1 --- Website Classification --- p.14Chapter 2.2.2 --- Web Unit Mining --- p.16Chapter 2.2.3 --- Logical Domain Extraction --- p.16Chapter 2.2.4 --- Web Thesaurus Construction --- p.17Chapter Chapter 3 --- Website Topic Hierarchy Generation --- p.19Chapter 3.1 --- Problem Definition --- p.19Chapter 3.2 --- Graph Based Algorithms --- p.21Chapter 3.2.1 --- Breadth First Search --- p.21Chapter 3.2.2 --- Shortest Path Search --- p.23Chapter 3.2.3 --- Minimum Directed Spanning Tree --- p.24Chapter 3.2.4 --- Discussion --- p.27Chapter 3.3 --- Edge Weight Function --- p.28Chapter 3.3.1 --- Relevance Method --- p.29Chapter 3.3.2 --- Machine Learning Method --- p.32Chapter 3.4 --- Experiments --- p.47Chapter 3.4.1 --- Data Preparation --- p.47Chapter 3.4.2 --- Performances of Breadth-first Search --- p.50Chapter 3.4.3 --- Performances of Shortest-path Search --- p.50Chapter 3.4.4 --- Performances of Directed Minimum Spanning Tree --- p.54Chapter 3.4.5 --- Comparison of Different Algorithms --- p.55Chapter Chapter 4 --- Website Summarization Through Keyphrase Extraction --- p.58Chapter 4.1 --- Introduction --- p.58Chapter 4.2 --- Background --- p.60Chapter 4.3 --- Keyphrase Extraction --- p.69Chapter 4.3.1 --- Candidate Phrases Idenfication --- p.69Chapter 4.3.2 --- Feature Calculation without Topic Hierarchy --- p.70Chapter 4.3.3 --- Feature Calculation with Topic Hierarchy --- p.72Chapter 4.3.4 --- Extraction of Keyphrases --- p.75Chapter 4.4 --- Experiments --- p.76Chapter Chapter 5 --- Conclusion and Future Work --- p.82References: --- p.8

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Text-image synergy for multimodal retrieval and annotation

Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.Text and images are the two most common data modalities found on the Internet. Understanding the synergy between text and images, that is, seamlessly analyzing information from these modalities may be trivial for humans, but is challenging for software systems. In this dissertation we study problems where deciphering text-image synergy is crucial for finding solutions. We propose methods and ideas that establish semantic connections between text and images in multimodal contents, and empirically show their effectiveness in four interconnected problems: Image Retrieval, Image Tag Refinement, Image-Text Alignment, and Image Captioning. Our promising results and observations open up interesting scopes for future research involving text-image data understanding.Text und Bild sind die beiden häufigsten Arten von Inhalten im Internet. Während es für Menschen einfach ist, gerade aus dem Zusammenspiel von Text- und Bildinhalten Informationen zu erfassen, stellt diese kombinierte Darstellung von Inhalten Softwaresysteme vor große Herausforderungen. In dieser Dissertation werden Probleme studiert, für deren Lösung das Verständnis des Zusammenspiels von Text- und Bildinhalten wesentlich ist. Es werden Methoden und Vorschläge präsentiert und empirisch bewertet, die semantische Verbindungen zwischen Text und Bild in multimodalen Daten herstellen. Wir stellen in dieser Dissertation vier miteinander verbundene Text- und Bildprobleme vor: • Bildersuche. Ob Bilder anhand von textbasierten Suchanfragen gefunden werden, hängt stark davon ab, ob der Text in der Nähe des Bildes mit dem der Anfrage übereinstimmt. Bilder ohne textuellen Kontext, oder sogar mit thematisch passendem Kontext, aber ohne direkte Übereinstimmungen der vorhandenen Schlagworte zur Suchanfrage, können häufig nicht gefunden werden. Zur Abhilfe schlagen wir vor, drei Arten von Informationen in Kombination zu nutzen: visuelle Informationen (in Form von automatisch generierten Bildbeschreibungen), textuelle Informationen (Stichworte aus vorangegangenen Suchanfragen), und Alltagswissen. • Verbesserte Bildbeschreibungen. Bei der Objekterkennung durch Computer Vision kommt es des Öfteren zu Fehldetektionen und Inkohärenzen. Die korrekte Identifikation von Bildinhalten ist jedoch eine wichtige Voraussetzung für die Suche nach Bildern mittels textueller Suchanfragen. Um die Fehleranfälligkeit bei der Objekterkennung zu minimieren, schlagen wir vor Alltagswissen einzubeziehen. Durch zusätzliche Bild-Annotationen, welche sich durch den gesunden Menschenverstand als thematisch passend erweisen, können viele fehlerhafte und zusammenhanglose Erkennungen vermieden werden. • Bild-Text Platzierung. Auf Internetseiten mit Text- und Bildinhalten (wie Nachrichtenseiten, Blogbeiträge, Artikel in sozialen Medien) werden Bilder in der Regel an semantisch sinnvollen Positionen im Textfluss platziert. Wir nutzen dies um ein Framework vorzuschlagen, in dem relevante Bilder ausgesucht werden und mit den passenden Abschnitten eines Textes assoziiert werden. • Bildunterschriften. Bilder, die als Teil von multimodalen Inhalten zur Verbesserung der Lesbarkeit von Texten dienen, haben typischerweise Bildunterschriften, die zum Kontext des umgebenden Texts passen. Wir schlagen vor, den Kontext beim automatischen Generieren von Bildunterschriften ebenfalls einzubeziehen. Üblicherweise werden hierfür die Bilder allein analysiert. Wir stellen die kontextbezogene Bildunterschriftengenerierung vor. Unsere vielversprechenden Beobachtungen und Ergebnisse eröffnen interessante Möglichkeiten für weitergehende Forschung zur computergestützten Erfassung des Zusammenspiels von Text- und Bildinhalten

Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic Embedding

Unsupervised discovery of stories with correlated news articles in real-time helps people digest massive news streams without expensive human annotations. A common approach of the existing studies for unsupervised online story discovery is to represent news articles with symbolic- or graph-based embedding and incrementally cluster them into stories. Recent large language models are expected to improve the embedding further, but a straightforward adoption of the models by indiscriminately encoding all information in articles is ineffective to deal with text-rich and evolving news streams. In this work, we propose a novel thematic embedding with an off-the-shelf pretrained sentence encoder to dynamically represent articles and stories by considering their shared temporal themes. To realize the idea for unsupervised online story discovery, a scalable framework USTORY is introduced with two main techniques, theme- and time-aware dynamic embedding and novelty-aware adaptive clustering, fueled by lightweight story summaries. A thorough evaluation with real news data sets demonstrates that USTORY achieves higher story discovery performances than baselines while being robust and scalable to various streaming settings.Comment: Accepted by SIGIR'2

Towards Personalized and Human-in-the-Loop Document Summarization

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.Comment: PhD thesi

ALens: An Adaptive Domain-Oriented Abstract Writing Training Tool for Novice Researchers

The significance of novice researchers acquiring proficiency in writing abstracts has been extensively documented in the field of higher education, where they often encounter challenges in this process. Traditionally, students have been advised to enroll in writing training courses as a means to develop their abstract writing skills. Nevertheless, this approach frequently falls short in providing students with personalized and adaptable feedback on their abstract writing. To address this gap, we initially conducted a formative study to ascertain the user requirements for an abstract writing training tool. Subsequently, we proposed a domain-specific abstract writing training tool called ALens, which employs rhetorical structure parsing to identify key concepts, evaluates abstract drafts based on linguistic features, and employs visualization techniques to analyze the writing patterns of exemplary abstracts. A comparative user study involving an alternative abstract writing training tool has been conducted to demonstrate the efficacy of our approach.Comment: Accepted by HHME/CHCI 202

Utilizing graph-based representation of text in a hybrid approach to multiple documents summarization

The aim of automatic text summarization is to process text with the purpose of identifying and presenting the most important information appearing in the text. In this research, we aim to investigate automatic multiple document summarization using a hybrid approach of extractive and “shallow abstractive methods. We aim to utilize the graph-based representation approach proposed in [1] and [2] as part of our method to multiple document summarization aiming to provide concise, informative and coherent summaries. We start by scoring sentences based on significance to extract top scoring ones from each document of the set of documents being summarized. In this step, we look into different criteria of scoring sentences, which include: the presence of highly frequent words of the document, the presence of highly frequent words of the set of documents and the presence of words found in the first and last sentence of the document and the different combination of such features. Upon running our experiments we found that the best combination of features to use is utilizing the presence of highly frequent words of the document and presence of words found in the first and last sentences of the document. The average f-score of those features had an average of 7.9% increase to other features\u27 f-scores. Secondly, we address the issue of redundancy of information through clustering sentences of same or similar information into one cluster that will be compressed into one sentence, thus avoiding redundancy of information as much as possible. We investigated clustering the extracted sentences based on two criteria for similarity, the first of which uses word frequency vector for similarity measure and the second of which uses word semantic similarity. Through our experiment, we found that the use of the word vector features yields much better clusters in terms of sentence similarity. The word feature vector had a 20% more number of clusters labeled to contain similar sentences as opposed to those of the word semantic feature. We then adopted a graph-based representation of text proposed in [1] and [2] to represent each sentence in a cluster, and using the k-shortest paths we found the shortest path to represent the final compressed sentence and use it as a final sentence in the summary. Human evaluator scored sentences based on grammatical correctness and almost 74% of 51 sentences evaluated got a perfect score of 2 which is a perfect or near perfect sentence. We finally propose a method for scoring the compressed sentences according to the order in which they should appear in the final summary. We used the Document Understanding Conference dataset for year 2014 as the evaluating dataset for our final system. We used the ROUGE system for evaluation which stands for Recall-Oriented Understudy for Gisting Evaluation. This system compare the automatic summaries to “ideal human references. We also compared our summaries ROUGE scores to those of summaries generated using the MEAD summarization tool. Our system provided better precision and f-score as well as comparable recall scores. On average our system has a percentage increase of 2% for precision and 1.6% increase in f-score than those of MEAD while MEAD has an increase of 0.8% in recall. In addition, our system provided more compressed version of the summary as opposed to that generated by MEAD. We finally ran an experiment to evaluate the order of sentences in the final summary and its comprehensibility where we show that our ordering method produced a comprehensible summary. On average, summaries that scored a perfect score in term of comprehensibility constitute 72% of the evaluated summaries. Evaluators were also asked to count the number of ungrammatical and incomprehensible sentences in the evaluated summaries and on average they were only 10.9% of the summaries sentences. We believe our system provide a \u27shallow abstractive summary to multiple documents that does not require intensive Natural Language Processing.

A Biased Topic Modeling Approach for Case Control Study from Health Related Social Media Postings

abstract: Online social networks are the hubs of social activity in cyberspace, and using them to exchange knowledge, experiences, and opinions is common. In this work, an advanced topic modeling framework is designed to analyse complex longitudinal health information from social media with minimal human annotation, and Adverse Drug Events and Reaction (ADR) information is extracted and automatically processed by using a biased topic modeling method. This framework improves and extends existing topic modelling algorithms that incorporate background knowledge. Using this approach, background knowledge such as ADR terms and other biomedical knowledge can be incorporated during the text mining process, with scores which indicate the presence of ADR being generated. A case control study has been performed on a data set of twitter timelines of women that announced their pregnancy, the goals of the study is to compare the ADR risk of medication usage from each medication category during the pregnancy. In addition, to evaluate the prediction power of this approach, another important aspect of personalized medicine was addressed: the prediction of medication usage through the identification of risk groups. During the prediction process, the health information from Twitter timeline, such as diseases, symptoms, treatments, effects, and etc., is summarized by the topic modelling processes and the summarization results is used for prediction. Dimension reduction and topic similarity measurement are integrated into this framework for timeline classification and prediction. This work could be applied to provide guidelines for FDA drug risk categories. Currently, this process is done based on laboratory results and reported cases. Finally, a multi-dimensional text data warehouse (MTD) to manage the output from the topic modelling is proposed. Some attempts have been also made to incorporate topic structure (ontology) and the MTD hierarchy. Results demonstrate that proposed methods show promise and this system represents a low-cost approach for drug safety early warning.Dissertation/ThesisDoctoral Dissertation Computer Science 201