Discourse oriented summarization

The meaning of text appears to be tightly related to intentions and circumstances. Context sensitivity of meaning is addressed by theories of discourse structure. Few attempts have been made to exploit text organization in summarization. This thesis is an exploration of what knowledge of discourse structure can do for content selection as a subtask of automatic summarization, and query-based summarization in particular. Query-based summarization is the task of answering an arbitrary user query or question by using content from potentially relevant sources. This thesis presents a general framework for discourse oriented summarization, relying on graphs to represent semantic relations in discourse, and redundancy as a special type of semantic relation. Semantic relations occur on several levels of text analysis (query-relevance, coherence, layout, etc.), and a broad range of textual features may be required to detect them. The graph-based framework facilitates combining multiple features into an integrated semantic model of the documents to summarize. Recognizing redundancy and entailment relations between text passages is particularly important when a summary is generated of multiple documents, e.g. to avoid including redundant content in a summary. For this reason, I pay particular attention to recognizing textual entailment. Within this framework, a three-fold evaluation is performed to evaluate different aspects of discourse oriented summarization. The first is a user study, measuring the effect on user appreciation of using a particular type of knowledge for query-based summarization. In this study, three presentation strategies are compared: summarization using the rhetorical structure of the source, a baseline summarization method which uses the layout of the source, and a baseline presentation method which uses no summarization but just a concise answer to the query. Results show that knowledge of the rhetorical structure not only helps to provide the necessary context for the user to verify that the summary addresses the query adequately, but also to increase the amount of relevant content. The second evaluation is a comparison of implementations of the graph-based framework which are capable of fully automatic summarization. The two variables in the experiment are the set of textual features used to model the source and the algorithm used to search a graph for relevant content. The features are based on cosine similarity, and are realized as graph representations of the source. The graph search algorithms are inspired by existing algorithms in summarization. The quality of summaries is measured using the Rouge evaluation toolkit. The best performer would have ranked first (Rouge-2) or second (Rouge-SU4) if it had participated in the DUC 2005 query-based summarization challenge. The third study is an evaluation in the context of the DUC 2006 summarization challenge, which includes readability measurements as well as various content-based evaluation metrics. The evaluated automatic discourse oriented summarization system is similar to the one described above, but uses additional features, i.e. layout and textual entailment. The system performed well on readability at the cost of content-based scores which were well below the scores of the highest ranking DUC 2006 participant. This indicates a trade-off between readable, coherent content and useful content, an issue yet to be explored. Previous research implies that theories of text organization generalize well to multimedia. This suggests that the discourse oriented summarization framework applies to summarizing multimedia as well, provided sufficient knowledge of the organization of the (multimedia) source documents is available. The last study in this thesis is an investigation of the applicability of structural relations in multimedia for generating picture-illustrated summaries, by relating summary content to picture-associated text (i.e. captions or surrounding paragraphs). Results suggest that captions are the more suitable annotation for selecting appropriate pictures. Compared to manual illustration, results of automatic pictures are similar if the manual picture is mainly decorative

Example-based controlled translation

The first research on integrating controlled language data in an Example-Based Machine Translation (EBMT) system was published in [Gough & Way, 2003]. We improve on their sub-sentential alignment algorithm to populate the system’s databases with more than six times as many potentially useful fragments. Together with two simple novel improvements—correcting mistranslations in the lexicon, and allowing multiple translations in the lexicon—translation quality improves considerably when target language translations are constrained. We also develop the first EBMT system which attempts to filter the source language data using controlled language specifications. We provide detailed automatic and human evaluations of a number of experiments carried out to test the quality of the system. We observe that our system outperforms Logomedia in a number of tests. Finally, despite conflicting results from different automatic evaluation metrics, we observe a preference for controlling the source data rather than the target translations

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

A Study of Realtime Summarization Metrics

Unexpected news events, such as natural disasters or other human tragedies, create a large volume of dynamic text data from official news media as well as less formal social media. Automatic real-time text summarization has become an important tool for quickly transforming this overabundance of text into clear, useful information for end-users including affected individuals, crisis responders, and interested third parties. Despite the importance of real-time summarization systems, their evaluation is not well understood as classic methods for text summarization are inappropriate for real-time and streaming conditions. The TREC 2013-2015 Temporal Summarization (TREC-TS) track was one of the first evaluation campaigns to tackle the challenges of real-time summarization evaluation, introducing new metrics, ground-truth generation methodology and dataset. In this paper, we present a study of TREC-TS track evaluation methodology, with the aim of documenting its design, analyzing its effectiveness, as well as identifying improvements and best practices for the evaluation of temporal summarization systems