In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization.
With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.
In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources.
Using physics models can be inaccurate because they cannot account for unknown factors and the effects of the deformation of the object as it is launched; moreover, deriving force coefficients for these models is not possible without extensive experimental testing.
In this paper, we propose LPCG (LiDAR point cloud guided monocular 3D object detection), which is a general framework for guiding the training of monocular 3D detectors with LiDAR point clouds.
We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users' needs.
Our experiments show that CATE is beneficial to the downstream search, especially in the large search space.
The fully differentiable fluid dynamics is integrated with a novel suction model for effective model predictive control of the tool.
We demonstrate that it is the unraveling of the tilted quantum master equation.
Statistical Mechanics Probability
Specifically, we present a novel graph memory mechanism to perform relational reasoning, and further develop two types of graph memory: a) visual graph memory that leverages visual information of video for relational reasoning; b) semantic graph memory that is specifically designed to explicitly leverage semantic knowledge contained in the classes and attributes of video objects, and perform relational reasoning in the semantic space.
Instead, we investigate several less-studied aspects of neural abstractive summarization, including (i) the importance of selecting important segments from transcripts to serve as input to the summarizer; (ii) striking a balance between the amount and quality of training instances; (iii) the appropriate summary length and start/end points.
We then analyze the performance of a meeting summarization system with and without jargon terms.
no code implementations • 17 Oct 2020 • Mariusz Bojarski, Chenyi Chen, Joyjit Daw, Alperen Değirmenci, Joya Deri, Bernhard Firner, Beat Flepp, Sachin Gogri, Jesse Hong, Lawrence Jackel, Zhenhua Jia, BJ Lee, Bo Liu, Fei Liu, Urs Muller, Samuel Payne, Nischal Kota Nagendra Prasad, Artem Provodin, John Roach, Timur Rvachov, Neha Tadimeti, Jesper van Engelen, Haiguang Wen, Eric Yang, Zongyi Yang
Four years ago, an experimental system known as PilotNet became the first NVIDIA system to steer an autonomous car along a roadway.
The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts.
We present an empirical study in favor of a cascade architecture to neural text summarization.
As a combination of various kinds of technologies, autonomous vehicles could complete a series of driving tasks by itself, such as perception, decision-making, planning, and control.
In this paper, we apply pre-trained language models to the Semantic Textual Similarity (STS) task, with a specific focus on the clinical domain.
We create a dataset containing the documents, source and fusion sentences, and human annotations of points of correspondence between sentences.
In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification.
Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs).
To enable flexible model coupling in coastal inundation studies, a coupling framework based on ESMF/NUOPC technology under a common modeling framework called the NOAA Environmental Modeling System (NEMS) was developed.
Atmospheric and Oceanic Physics
Machine learning (ML) methods have gained increasing popularity in exploring and developing new materials.
In order to come up with a better representation and capturing of long term spatio-temporal relationships, we propose three variants of Self-Attention Network (SAN), namely, SAN-V1, SAN-V2 and SAN-V3.
Ranked #44 on Skeleton Based Action Recognition on NTU RGB+D
In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones.
Ranked #10 on Text Summarization on GigaWord
If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged.
Ranked #21 on Text Summarization on GigaWord
Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion.
While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences.
A robust evaluation metric has a profound impact on the development of text generation systems.
There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs.
The most important obstacles facing multi-document summarization include excessive redundancy in source descriptions and the looming shortage of training data.
Limited angular resolution has become the main bottleneck of microlens-based plenoptic cameras towards practical vision applications.
Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora.
Ranked #30 on Named Entity Recognition on CoNLL 2003 (English)
Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events.
In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence.
Ranked #28 on Text Summarization on GigaWord
Generating an abstract from a collection of documents is a desirable capability for many real-world applications.
We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR).
Teaching large classes remains a great challenge, primarily because it is difficult to attend to all the student needs in a timely manner.
We use reinforcement learning to explore the space of possible extractive summaries and introduce a question-focused reward function to promote concise, fluent, and informative summaries.
While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) --- extraction of fine-grained opinion polarity w. r. t.
Ranked #3 on Aspect-Based Sentiment Analysis on Sentihood
Despite successful applications across a broad range of NLP tasks, conditional random fields ("CRFs"), in particular the linear-chain variant, are only able to model local features.
The base classifier of the ensemble method is a modified k-means algorithm.
Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e. g. linear regression or Support Vector Machines.
Many methods have been used to recognize author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e. g. linear regression or Support Vector Machines.
In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules.
This paper describes an experimental approach to Detection of Minimal Semantic Units and their Meaning (DiMSUM), explored within the framework of SemEval 2016 Task 10.