Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification

Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC include adding a label "blank" during training. With this mechanism, CTC eliminates the need of segment alignment, and hence has been applied to various sequence-to-sequence learning problems. In this work, we applied CTC to abstractive summarization for spoken content. The "blank" in this case implies the corresponding input data are less important or noisy; thus it can be ignored. This approach was shown to outperform the existing methods in term of ROUGE scores over Chinese Gigaword and MATBN corpora. This approach also has the nice property that the ordering of words or characters in the input documents can be better preserved in the generated summaries.Comment: Accepted by Interspeech 201

Abstract Meaning Representation for Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.Comment: 13 page

Self-Supervised and Controlled Multi-Document Opinion Summarization

We address the problem of unsupervised abstractive summarization of collections of user generated reviews with self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries.Finally, we extend the Transformer architecture to allow for multiple reviews as input. Our benchmarks on two datasets against graph-based and recent neural abstractive unsupervised models show that our proposed method generates summaries with a superior quality and relevance.This is confirmed in our human evaluation which focuses explicitly on the faithfulness of generated summaries We also provide an ablation study, which shows the importance of the control setup in controlling hallucinations and achieve high sentiment and topic alignment of the summaries with the input reviews.Comment: 18 pages including 5 pages appendi

Circumnavigation of an Unknown Target Using UAVs with Range and Range Rate Measurements

This paper presents two control algorithms enabling a UAV to circumnavigate an unknown target using range and range rate (i.e., the derivative of range) measurements. Given a prescribed orbit radius, both control algorithms (i) tend to drive the UAV toward the tangent of prescribed orbit when the UAV is outside or on the orbit, and (ii) apply zero control input if the UAV is inside the desired orbit. The algorithms differ in that, the first algorithm is smooth and unsaturated while the second algorithm is non-smooth and saturated. By analyzing properties associated with the bearing angle of the UAV relative to the target and through proper design of Lyapunov functions, it is shown that both algorithms produce the desired orbit for an arbitrary initial state. Three examples are provided as a proof of concept.Comment: To appear in IEEE Conference on Decision and Control, 201

Generative AI in Education From the Perspective of Students, Educators, and Administrators

This research explores how advanced artificial intelligence (AI), like the technology that powers tools such as ChatGPT, is changing the way we teach and learn in schools and universities. Imagine AI helping to summarize thick legal documents into something you can read over a coffee break or helping students learn how to code by offering personalized guidance. We looked into how teachers feel about using these AI tools in their classrooms, what kind of rules schools have about them, and how they can make learning programming easier for students. We found that most teachers are excited about the possibilities but also a bit cautious because they want to make sure these tools are used fairly and safely. There’s also a lot that schools need to figure out in terms of setting up the right rules to make the best use of AI. Our study suggests that if we can address these challenges, AI could make education more engaging, accessible, and effective for everyone. It’s a call to educators, policymakers, and tech developers to work together to ensure AI tools are used in ways that benefit all students and help prepare them for a future where technology plays an even bigger role in our lives

Latent protein trees

Unbiased, label-free proteomics is becoming a powerful technique for measuring protein expression in almost any biological sample. The output of these measurements after preprocessing is a collection of features and their associated intensities for each sample. Subsets of features within the data are from the same peptide, subsets of peptides are from the same protein, and subsets of proteins are in the same biological pathways, therefore, there is the potential for very complex and informative correlational structure inherent in these data. Recent attempts to utilize this data often focus on the identification of single features that are associated with a particular phenotype that is relevant to the experiment. However, to date, there have been no published approaches that directly model what we know to be multiple different levels of correlation structure. Here we present a hierarchical Bayesian model which is specifically designed to model such correlation structure in unbiased, label-free proteomics. This model utilizes partial identification information from peptide sequencing and database lookup as well as the observed correlation in the data to appropriately compress features into latent proteins and to estimate their correlation structure. We demonstrate the effectiveness of the model using artificial/benchmark data and in the context of a series of proteomics measurements of blood plasma from a collection of volunteers who were infected with two different strains of viral influenza.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS639 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Summary Execution: The Impact of Alternative Summarization Strategies on Local Governments

Performance management in the public sector, including local government, has become far more pervasive in recent decades. Often performance indicators are summarized into a single score to enhance understanding and ease dissemination. However, the summation of performance indicators caries a risk that the rating assigned may largely be an artefact of the summarization strategy rather than an accurate representation of municipal performance. We employ the recent evaluation of New South Wales’ municipal performance to demonstrate that the performance indicator compilation strategy is indeed a major determinant of the ratings assigned to local councils. Moreover, we illustrate how ratings may exert a constitutive effect on municipalities by altering organizational behavior. A number of policy lessons are drawn from our empirical analysis, including significant methodological considerations and the need for higher levels of transparency

Long-term and persistent vocal plasticity in adult bats.

Bats exhibit a diverse and complex vocabulary of social communication calls some of which are believed to be learned during development. This ability to produce learned, species-specific vocalizations - a rare trait in the animal kingdom - requires a high-degree of vocal plasticity. Bats live extremely long lives in highly complex and dynamic social environments, which suggests that they might also retain a high degree of vocal plasticity in adulthood, much as humans do. Here, we report persistent vocal plasticity in adult bats (Rousettus aegyptiacus) following exposure to broad-band, acoustic perturbation. Our results show that adult bats can not only modify distinct parameters of their vocalizations, but that these changes persist even after noise cessation - in some cases lasting several weeks or months. Combined, these findings underscore the potential importance of bats as a model organism for studies of vocal plasticity, including in adulthood