摘要Abstract怎么写
一、摘要Abstract是什么?
一篇论文的摘要主要涵盖文章的关键问题和重要发现。它的内容简短、形式精炼,通常作为文章的第一段,概括导览整篇论文的重要内容,是一篇论文的缩略图。可以说摘要是论文最重要的部分,因为论文的绝大多数读者都会首先阅读摘要的内容。摘要的质量往往也影响着读者是否选择继续阅读的决定。
二、摘要Abstract怎么写?
摘要通常包含以下内容:
- 介绍课题背景或研究目的
- 研究内容和重要性
- 研究方法的描述,例如实验方法、观察方法、评估方法等(可选)
- 主要发现和结论
- 研究的影响或未来的方向(可选)
课题背景或研究目的
长度为1-2句,对课题的背景的介绍,可以是介绍课题关键词的概念含义,也可以是课题相关领域的最新发展和应用。例如:
Deep learning, as one of the most currently remarkable machine learning techniques, has achieved great success in many applications such as image analysis, speech recognition and text understanding.
或在介绍完大的课题背景后,转向对本文内容、目的的介绍,指出阐明这篇论文的位置,例如:
This work aims to review the state-of-the-art in deep learning algorithms in computer vision by highlighting the contributions and challenges from over 210 recent research papers.
研究的重要性和主要内容
长度为1-2句,这部分通常可以以转折开始,可以指出之前工作的不足之处,或当下领域内面临的挑战,来铺垫反衬出这篇论文的独特性。例如:
However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective.
转折之后,开始引入介绍这篇文章的主要内容,使人耳目一新,例如:
This article presents a comprehensive review of historical and recent state-of-the art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications.
研究方法的描述
长度为2-3句,在这里,作者会指出这篇论文提出了什么样的模型,运用了什么样的框架,这些方法有哪些创新之处等,例如:
Here, we derive a phase-coded representation of the memory state, Rotational Unit of Memory (RUM), that unifies the concepts of unitary learning and associative memory.
主要发现和结论
长度为1-2句,可以是之前描述的研究方法的实验结果,提出的方法和已有方法的优势对比,也可以是在实验中的重要发现,例如:
We show experimentally that RNNs based on RUMs can solve basic sequential tasks such as memory copying and memory recall much better than LSTMs/GRUs.
研究的影响或未来的方向
长度为1-2句,这部分对应了文章中结论conclusion部分的内容,作为延申和总结,例如:
A list of future research topics are finally given with clear justifications.
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参考文献:
- Zhang, S., Yao, L., Sun, A. & Tay, Y. 2019, 'Deep learning based recommender system: A survey and new perspectives', ACM Computing Surveys (CSUR), vol. 52, no. 1, p. 5.
- Zhang, Q., Yang, L.T., Chen, Z. & Li, P. 2018, 'A survey on deep learning for big data', Information Fusion, vol. 42, pp. 146-57.
- Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M.P., Shyu, M.-L., Chen, S.-C. & Iyengar, S. 2018, 'A survey on deep learning: Algorithms, techniques, and applications', ACM Computing Surveys (CSUR), vol. 51, no. 5, p. 92.
- Dangovski, R., Jing, L., Nakov, P., Tatalović, M. & Soljačić, M. 2019, 'Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications', Transactions of the Association for Computational Linguistics, vol. 7, pp. 121-38.
编辑于 2019-05-06 12:14