A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A new method uses PSO to optimize sentence weights for user-oriented document summaries.
This guide simplifies explainable deep learning for beginners.
Improved text summarization using belief propagation on weighted bipartite graphs.
SuTaT creates dialogue summaries for tete-a-tetes without labeled data.
In this work, we study abstractive text summarization by exploring different models such as LSTM-encoder-decoder with attention, pointer-generator networks, coverage mechanisms, and transformers. Upon extensive and careful hyperparameter tuning we compare the proposed architectures against each other for the abstractiv…
Patient summarization is essential for clinicians to provide coordinated care and practice effective communication. Automated summarization has the potential to save time, standardize notes, aid clinical decision making, and reduce medical errors. Here we provide an upper bound on extractive summarization of discharge …
BERT helps summarize lecture content efficiently.
Improves document summarization by combining word embeddings and n-grams.
Transformer model improves source code summarization.
Automatic summarization of natural language is a current topic in computer science research and industry, studied for decades because of its usefulness across multiple domains. For example, summarization is necessary to create reviews such as this one. Research and applications have achieved some success in extractive …
Balancing graph summarization and change detection in streaming data.
Study improves summarization reliability in risky scenarios.
Proposes RDASS for better Korean text summarization evaluation.
This study tackles Gaussian process regression with summarized data.
A new method to protect enterprise data privacy in AI models.
Automates summarizing federal grant audits with machine learning.
In the past few years, neural abstractive text summarization with sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many interesting techniques have been proposed to improve seq2seq models, making them capable of handling different challenges, such as saliency, fluency and human readability, and ge…
AI agents are being developed to support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understand their behavior. Policy summarization methods aim to convey strengths and weaknesses of such agents by demonstrating their behavior in a su…
Paper introduces methods to automatically generate SOAP notes from patient-physician conversations.
In this paper we introduce a micro-clustering strategy for Functional Boxplots. The aim is to summarize a set of streaming time series splitted in non overlapping windows. It is a two step strategy which performs at first, an on-line summarization by means of functional data structures, named Functional Boxplot micro-c…
This paper presents a method to summarize directed graphs while preserving edge information.
Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph compone…
The sheer scale of modern datasets has resulted in a dire need for summarization techniques that identify representative elements in a dataset. Fortunately, the vast majority of data summarization tasks satisfy an intuitive diminishing returns condition known as submodularity, which allows us to find nearly-optimal sol…
Improved text summarization using neural semantic encoders with hierarchical structure.
Improved source code summarization using extended Tree-LSTM.
SummerTime summarizes variable-length time series for machine learning applications.
Few summaries enable automatic summarization of product reviews.
Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets. Transfer learning is a potential solution but their effectiveness in the text domain is not as explored as in areas such as image analysis. In this paper, we study the problem of transfer learning for…
Descriptive comments play a crucial role in the software engineering process. They decrease development time, enable better bug detection, and facilitate the reuse of previously written code. However, comments are commonly the last of a software developer's priorities and are thus either insufficient or missing entirel…
We address the problem of maximizing an unknown submodular function that can only be accessed via noisy evaluations. Our work is motivated by the task of summarizing content, e.g., image collections, by leveraging users' feedback in form of clicks or ratings. For summarization tasks with the goal of maximizing coverage…
New method reduces labeling costs in semi-supervised learning.
It is now much easier than ever before to produce videos. While the ubiquitous video data is a great source for information discovery and extraction, the computational challenges are unparalleled. Automatically summarizing the videos has become a substantial need for browsing, searching, and indexing visual content. Th…
Summarizes financial news for better investment decisions.
In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization. Through jointly attending to topics and word-level alignment, o…
Summarizes geometric connections between sigma models and Gross-Neveu models.
A new algorithm ThreeSieves maximizes submodular functions efficiently in streaming data.
We introduce a method to learn a mixture of submodular "shells" in a large-margin setting. A submodular shell is an abstract submodular function that can be instantiated with a ground set and a set of parameters to produce a submodular function. A mixture of such shells can then also be so instantiated to produce a mor…
Centroid Transformers reduce memory and computation by summarizing inputs into centroids.
SemSentSum uses embeddings to link facts across documents efficiently.
Unsupervised summarization generates novel reviews reflecting consensus opinions.
This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows si…
We summarize results concerning the Bernstein property of differential equations.
The Blaschke conjecture claims that every compact Riemannian manifold whose injectivity radius equals its diameter is, up to constant rescaling, a compact rank one symmetric space. We summarize the intuition behind this problem, the proof that such manifolds have the cohomology of compact rank one symmetric spaces, and…
This paper improves collaborative filtering by integrating user and item embeddings with attention.
Given vector representations for individual words, it is necessary to compute vector representations of sentences for many applications in a compositional manner, often using artificial neural networks. Relatively little work has explored the internal structure and properties of such sentence vectors. In this paper, we…
New method for summarizing Bayesian mixture models using sliced Wasserstein distances.
A new model generates summaries by conditioning on input text and latent topics.
Summarizes changepoint detection models and their applications.