A Survey on Event-based News Narrative Extraction

Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened over 900 articles that yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.Comment: 37 pages, 3 figures, to be published in the journal ACM CSU

Methods of sentence extraction, abstraction and ordering for automatic text summarization

In this thesis, we have developed several techniques for tackling both the extractive and abstractive text summarization tasks. We implement a rank based extractive sentence selection algorithm. For ensuring a pure sentence abstraction, we propose several novel sentence abstraction techniques which jointly perform sentence compression, fusion, and paraphrasing at the sentence level. We also model abstractive compression generation as a sequence-to-sequence (seq2seq) problem using an encoder-decoder framework. Furthermore, we applied our sentence abstraction techniques to the multi-document abstractive text summarization. We also propose a greedy sentence ordering algorithm to maintain the summary coherence for increasing the readability. We introduce an optimal solution to the summary length limit problem. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods. At the end of this thesis, we also introduced a new concept called "Reader Aware Summary" which can generate summaries for some critical readers (e.g. Non-Native Reader).Natural Sciences and Engineering Research Council (NSERC) of Canada and the University of Lethbridg

Automatic Generation of Story Highlights

In this paper we present a joint content selection and compression model for single-document summarization. The model operates over a phrase-based representation of the source document which we obtain by merging information from PCFG parse trees and dependency graphs. Using an integer linear programming formulation, the model learns to select and combine phrases subject to length, coverage and grammar constraints. We evaluate the approach on the task of generating “story highlights”—a small number of brief, self-contained sentences that allow readers to quickly gather information on news stories. Experimental results show that the model’s output is comparable to human-written highlights in terms of both grammaticality and content.

Complex question answering : minimizing the gaps and beyond

xi, 192 leaves : ill. ; 29 cmCurrent Question Answering (QA) systems have been significantly advanced in demonstrating finer abilities to answer simple factoid and list questions. Such questions are easier to process as they require small snippets of texts as the answers. However, there is a category of questions that represents a more complex information need, which cannot be satisfied easily by simply extracting a single entity or a single sentence. For example, the question: “How was Japan affected by the earthquake?” suggests that the inquirer is looking for information in the context of a wider perspective. We call these “complex questions” and focus on the task of answering them with the intention to minimize the existing gaps in the literature. The major limitation of the available search and QA systems is that they lack a way of measuring whether a user is satisfied with the information provided. This was our motivation to propose a reinforcement learning formulation to the complex question answering problem. Next, we presented an integer linear programming formulation where sentence compression models were applied for the query-focused multi-document summarization task in order to investigate if sentence compression improves the overall performance. Both compression and summarization were considered as global optimization problems. We also investigated the impact of syntactic and semantic information in a graph-based random walk method for answering complex questions. Decomposing a complex question into a series of simple questions and then reusing the techniques developed for answering simple questions is an effective means of answering complex questions. We proposed a supervised approach for automatically learning good decompositions of complex questions in this work. A complex question often asks about a topic of user’s interest. Therefore, the problem of complex question decomposition closely relates to the problem of topic to question generation. We addressed this challenge and proposed a topic to question generation approach to enhance the scope of our problem domain

Document Summarization and Classification using Concept and Context Similarity Analysis

ABSTRACT: "Document summarization and classification using concept and context similarity analysis'' deals with an information retrieval task, which aims at extracting a condensed version of the original document. A document summary is useful since it can give an overview of the original document in a shorter period of time. The main goal of a summary is to present the main ideas in a document/set of documents in a short and readable paragraph. Classification is a data mining function that assigns items in a collection to target categories of the documents. Context sensitive document indexing model based on the Bernoulli model of randomness is used for document summarization process. The lexical association between terms is used to produce a context sensitive weight to the document terms. The context sensitive indexing weights are used to compute the sentence similarity matrix and as a result, the sentences are presented in such a way that the most informative sentences appear on the top of the summary, making a positive impact on the quality of the summar

A reinforcement learning formulation to the complex question answering problem

International audienceWe use extractive multi-document summarization techniques to perform complex question answering and formulate it as a reinforcement learning problem. Given a set of complex questions, a list of relevant documents per question, and the corresponding human generated summaries (i.e. answers to the questions) as training data, the reinforcement learning module iteratively learns a number of feature weights in order to facilitate the automatic generation of summaries i.e. answers to previously unseen complex questions. A reward function is used to measure the similarities between the candidate (machine generated) summary sentences and the abstract summaries. In the training stage, the learner iteratively selects the important document sentences to be included in the candidate summary, analyzes the reward function and updates the related feature weights accordingly. The final weights are used to generate summaries as answers to unseen complex questions in the testing stage. Evaluation results show the effectiveness of our system. We also incorporate user interaction into the reinforcement learner to guide the candidate summary sentence selection process. Experiments reveal the positive impact of the user interaction component on the reinforcement learning framework

Multi-document summarization based on atomic semantic events and their temporal relationss

Automatic multi-document summarization (MDS) is the process of extracting the most important information such as events and entities from multiple natural language texts focused on the same topic. We extract all types of semantic atomic information and feed them to a topic model to experiment with their effects on a summary. We design a coherent summarization system by taking into account the sentence relative positions in the original text. Our generic MDS system has outperformed the best recent multi-document summarization system in DUC 2004 in terms of ROUGE-1 recall and $f_1$-measure. Our query-focused summarization system achieves a statistically similar result to the state-of-the-art unsupervised system for DUC 2007 query-focused MDS task in ROUGE-2 recall measure. Update Summarization is a new form of MDS where novel yet salience sentences are chosen as summary sentences based on the assumption that the user has already read a given set of documents. In this thesis, we present an event based update summarization where the novelty is detected based on the temporal ordering of events and the saliency is ensured by event and entity distribution. To our knowledge, no other study has deeply investigated the effects of the novelty information acquired from the temporal ordering of events (assuming that a sentence contains one or more events) in the domain of update MDS. Our update MDS system has outperformed the state-of-the-art update MDS system in terms of ROUGE-2, and ROUGE-SU4 recall measures. Our MDS systems also generate quality summaries which are manually evaluated based on popular evaluation criteria

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

Graph-based Patterns for Local Coherence Modeling

Coherence is an essential property of well-written texts. It distinguishes a multi-sentence text from a sequence of randomly strung sentences. The task of local coherence modeling is about the way that sentences in a text link up one another. Solving this task is beneficial for assessing the quality of texts. Moreover, a coherence model can be integrated into text generation systems such as text summarizers to produce coherent texts. In this dissertation, we present a graph-based approach to local coherence modeling that accounts for the connectivity structure among sentences in a text. Graphs give our model the capability to take into account relations between non-adjacent sentences as well as those between adjacent sentences. Besides, the connectivity style among nodes in graphs reflects the relationships among sentences in a text. We first employ the entity graph approach, proposed by Guinaudeau and Strube (2013), to represent a text via a graph. In the entity graph representation of a text, nodes encode sentences and edges depict the existence of a pair of coreferent mentions in sentences. We then devise graph-based features to capture the connectivity structure of nodes in a graph, and accordingly the connectivity structure of sentences in the corresponding text. We extract all subgraphs of entity graphs as features which encode the connectivity structure of graphs. Frequencies of subgraphs correlate with the perceived coherence of their corresponding texts. Therefore, we refer to these subgraphs as coherence patterns. In order to complete our approach to coherence modeling, we propose a new graph representation of texts, rather than the entity graph. Our approach employs lexico-semantic relations among words in sentences, instead of only entity coreference relations, to model relationships between sentences via a graph. This new lexical graph representation of text plus our method for mining coherence patterns make our coherence model. We evaluate our approach on the readability assessment task because a primary factor of readability is coherence. Coherent texts are easy to read and consequently demand less effort from their readers. Our extensive experiments on two separate readability assessment datasets show that frequencies of coherence patterns in texts correlate with the readability ratings assigned by human judges. By training a machine learning method on our coherence patterns, our model outperforms its counterparts on ranking texts with respect to their readability. As one of the ultimate goals of coherence models is to be used in text generation systems, we show how our coherence patterns can be integrated into a graph-based text summarizer to produce informative and coherent summaries. Our coherence patterns improve the performance of the summarization system based on both standard summarization metrics and human evaluations. An implementation of the approaches discussed in this dissertation is publicly available