Inferring Strategies for Sentence Ordering in Multidocument News Summarization

The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocument summarization where summary sentences may be drawn from different input articles. In this paper, we propose a methodology for studying the properties of ordering information in the news genre and describe experiments done on a corpus of multiple acceptable orderings we developed for the task. Based on these experiments, we implemented a strategy for ordering information that combines constraints from chronological order of events and topical relatedness. Evaluation of our augmented algorithm shows a significant improvement of the ordering over two baseline strategies

A Novel Method of Sentence Ordering Based on Support Vector Machine

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Unifying dimensions in coherence relations: How various annotation frameworks are related

In this paper, we show how three often used and seemingly different discourse annotation frameworks – Penn Discourse Treebank (PDTB), Rhetorical Structure Theory (RST), and Segmented Discourse Representation Theory – can be related by using a set of unifying dimensions. These dimensions are taken from the Cognitive approach to Coherence Relations and combined with more fine-grained additional features from the frameworks themselves to yield a posited set of dimensions that can successfully map three frameworks. The resulting interface will allow researchers to find identical or at least closely related relations within sets of annotated corpora, even if they are annotated within different frameworks. Furthermore, we tested our unified dimension (UniDim) approach by comparing PDTB and RST annotations of identical news- paper texts and converting their original end label annotations of relations into the accompanying values per dimension. Subsequently, rates of overlap in the attributed values per dimension were analyzed. Results indicate that the pro- posed dimensions indeed create an interface that makes existing annotation systems “talk to each other.

Extracting Temporal and Causal Relations between Events

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.Comment: PhD Thesi

The design and implementation of a system for the automatic generation of narrative debriefs for AUV Missions

Increased autonomy allows autonomous underwater vehicles to act without direct support or supervision. This requires increased complexity, however, and a deficit of trust may form between operators and these complex machines, though previous research has shown this can be reduced through repeated experience with the system in question. Regardless of whether a mission is performed with real vehicles or their simulated counterparts, effective debrief represents the most efficient method for performing an analysis of the mission. A novel system is presented to maximise the effectiveness of a debrief by ordering the mission events using a narrative structure, which has been shown to be the quickest and most effective way of communicating information and building a situation model inside a person’s mind. Mission logs are de-constructed and analysed, then optimisation algorithms used to generate a coherent discourse based on the events of the missions with any required exposition. This is then combined with a timed mission playback and additional visual information to form an automated mission debrief. This approach was contrasted with two alternative techniques: a simpler chronological ordering; and a facsimile of the current state of the art. Results show that participant recall accuracy was higher and the need for redundant delivery of information was lower when compared to either of the baselines. Also apparent is a need for debriefs to be adapted to individual users and scenarios. Results are discussed in full, along with suggestions for future avenues of research