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

Linked Data Entity Summarization

On the Web, the amount of structured and Linked Data about entities is constantly growing. Descriptions of single entities often include thousands of statements and it becomes difficult to comprehend the data, unless a selection of the most relevant facts is provided. This doctoral thesis addresses the problem of Linked Data entity summarization. The contributions involve two entity summarization approaches, a common API for entity summarization, and an approach for entity data fusion

Efficiently identifying top k similar entities

With the rapid growth in genomic studies, more and more successful researches are being produced that integrate tools and technologies from interdisciplinary sciences. Computational biology or bioinformatics is one such field that successfully applies computational tools to capture and transcribe biological data. Specifically in genomic studies, detection and analysis of co-occurring mutations is an leading area of study. Concurrently, in the recent years, computer science and information technology have seen an increased interest in the area association analysis and co-occurrence computation. The traditional method of finding top similar entities involves examining every possible pair of entities, which leads to a prohibitive quadratic time complexity. Most of the existing approaches also require a similarity measure and threshold beforehand to retrieve the top similar entities. These parameters are not always easy to tune. Heuristically, an adaptive method can have wider applications for identifying the top most similar pair of mutations (or entities in general). In this thesis, we have presented an algorithm to efficiently identify top k similar pair of mutations using co-occurrence as the similarity measure. Our approach used an upperbound condition to iteratively prune the search space and tackled the quadratic complexity. The empirical evaluations show that the proposed approach shows the computational efficiency in terms of execution time and accuracy of our approach particularly in large size datasets. In addition, we also evaluate the impact of various parameters like input size, k on the execution time in top k approaches. This study concludes that systematic pruning of the search space using an adaptive threshold condition optimizes the process of identifying top similar pair of entities

Bridging Low-level Geometry to High-level Concepts in Visual Servoing of Robot Manipulation Task Using Event Knowledge Graphs and Vision-Language Models

In this paper, we propose a framework of building knowledgeable robot control in the scope of smart human-robot interaction, by empowering a basic uncalibrated visual servoing controller with contextual knowledge through the joint usage of event knowledge graphs (EKGs) and large-scale pretrained vision-language models (VLMs). The framework is expanded in twofold: first, we interpret low-level image geometry as high-level concepts, allowing us to prompt VLMs and to select geometric features of points and lines for motor control skills; then, we create an event knowledge graph (EKG) to conceptualize a robot manipulation task of interest, where the main body of the EKG is characterized by an executable behavior tree, and the leaves by semantic concepts relevant to the manipulation context. We demonstrate, in an uncalibrated environment with real robot trials, that our method lowers the reliance of human annotation during task interfacing, allows the robot to perform activities of daily living more easily by treating low-level geometric-based motor control skills as high-level concepts, and is beneficial in building cognitive thinking for smart robot applications

On Mutual Authorizations: Semantics, Integration Issues, and Performance

reciprocity is a powerful determinant of human behavior. None of the existing access control models however captures this reciprocity phenomenon. In this paper, we introduce a new kind of grant, which we call mutual, to express authorizations that actually do this, i.e., users grant access to their resources only to users who allow them access to theirs. We define the syntax and semantics of mutual authorizations and show how this new grant can be included in the Role-Based Access Control model, i.e., extend RBAC with it. We use location-based services as an example to deploy mutual authorizations, and we propose two approaches to integrate them into these services. Next, we prove the soundness and analyze the complexity of both approaches. We also study how the ratio of mutual to allow and to deny authorizations affects the number of persons whose position a given person may read. These ratios may help in predicting whether users are willing to use mutual authorizations instead of deny or allow. Experiments confirm our complexity analysis and shed light on the performance of our approaches

Towards a Fully Unsupervised Framework for Intent Induction in Customer Support Dialogues

State of the art models in intent induction require annotated datasets. However, annotating dialogues is time-consuming, laborious and expensive. In this work, we propose a completely unsupervised framework for intent induction within a dialogue. In addition, we show how pre-processing the dialogue corpora can improve results. Finally, we show how to extract the dialogue flows of intentions by investigating the most common sequences. Although we test our work in the MultiWOZ dataset, the fact that this framework requires no prior knowledge make it applicable to any possible use case, making it very relevant to real world customer support applications across industry.Comment: 16 pages, 8 figure