摘要Abstract怎么写

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.

觉得有帮助就点个赞吧(。・ω・。)ノ♡

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