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CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19

JOURNAL OF SUPERCOMPUTING 2023年第14期79卷 16328-16350页

作者： Karotia, Akanksha Susan, Seba Delhi Technol Univ Dept Informat Technol Delhi 110042 India

The number of research articles published on COVID-19 has dramatically increased since the outbreak of the pandemic in November 2019. This absurd rate of productivity in research articles leads to information overload. It has increasingly become urgent for researchers and medical associations to stay up to date on the latest COVID-19 studies. To address information overload in COVID-19 scientific literature, the study presents a novel hybrid model named CovSumm, an unsupervised graph-based hybrid approach for single-document summarization, that is evaluated on the CORD-19 dataset. We have tested the proposed methodology on the scientific papers in the database dated from January 1, 2021 to December 31, 2021, consisting of 840 documents in total. The proposed text summarization is a hybrid of two distinctive extractive approaches (1) GenCompareSum (transformer-based approach) and (2) TextRank (graph-based approach). The sum of scores generated by both methods is used to rank the sentences for generating the summary. On the CORD-19, the recall-oriented understudy for gisting evaluation (ROUGE) score metric is used to compare the performance of the CovSumm model with various state-of-the-art techniques. The proposed method achieved the highest scores of ROUGE-1: 40.14%, ROUGE-2: 13.25%, and ROUGE-L: 36.32%. The proposed hybrid approach shows improved performance on the CORD-19 dataset when compared to existing unsupervised text summarization methods.

关键词： Unsupervised model graph-based technique Extractive summarization Document summarization COVID-19 scientific papers

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DNF: Feature Point Matching Pairs Filter based on Descriptor Net

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IEEE ACCESS 2019年 7卷 87561-87573页

作者： Ye, Feng Su, Lingfeng Lai, Yizong South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Guangdong Peoples R China

Wide-baseline image registration under out-of-plane rotation and larger viewpoint change is still challenging. Most of the commonly used matching algorithms are not invariant to affine transformation. They heavily rely on the local features of image patches and ignore global information, the mismatch is inevitable and greatly affect the accuracy of image registration. To address this issue, we propose a feature point matching pair filter based on global spatial position correspondences of feature points, coined Descriptor Net Filter (DNF). We put forward two criteria to evaluate matching quality. One is the local matching quality computed by independent local feature, the other is the global matching quality relying on geometric network constraint. Combining the advantages of both local feature and large-scale geometric constraint, our method removes mismatches effectively. The experiments on both planar scenes and 3D objects from several standard datasets show that the DNF significantly enhances the matching precision and retains more correct matches as well.

关键词： Image registration mismatch removal wide baseline matching graph-based technique

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An Approximate Algorithm for the Multiple Constant Multiplications Problem 08

An Approximate Algorithm for the Multiple Constant Multiplic...

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21st Symposium on Integrated Circuits and Systems Design (SBCCI 2008)

作者： Aksoy, Levent Gunes, Ece Olcay Istanbul Tech Univ Dept Elect & Commun Engn TR-34469 Istanbul Turkey

ISBN: (纸本)9781605582313

Multiple constant multiplications (MCM) problem that is to obtain the minimum number of addition/subtraction operations required to implement the constant multiplications finds itself and its variants in many applications, such as finite impulse response (FIR) filters, linear signal transforms, and computer arithmetic. There have been a number of efficient algorithms proposed for the MCM problem. However, due to the NP-hardness of the problem, the proposed algorithms have been heuristics and cannot guarantee the minimum solution. In this paper, we introduce an approximate algorithm that can ensure the minimum solution on more instances than the previously proposed heuristics and can be extended to an exact algorithm using an exhaustive search. The approximate algorithm has been applied on a comprehensive set of instances including FIR filter and randomly generated hard instances, and compared with the previously proposed efficient heuristics. It is observed from the experimental results that the proposed approximate algorithm finds competitive and better results than the prominent heuristics.

关键词： Multiple constant multiplications problem graph-based technique approximate algorithm multiplierless filter design

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