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所在平台: Udemy |
课程主页: https://www.udemy.com/course/text-summarization-natural-language-processing-python/
课程评论：没有评论
Coursera 课程 "面向文本摘要的自然语言处理" 概述： 本课程深入探讨自然语言处理（NLP）这一人工智能分支，旨在教授计算机理解人类语言的能力。课程内容涵盖了文本摘要这一重要应用，即自动生成文本的简短摘要。 **课程亮点：** * **核心算法讲解与实践：** 详细讲解并亲手实现三种经典的文本摘要算法： * 基于词频的算法 * 基于距离的算法（结合 PageRank 的余弦相似度） * Luhn 算法（早期文本摘要方法的代表） * **现代技术赋能：** 使用 Python 编程语言、NLTK 和 spaCy 库，并在 Google Colab 环境中进行实践，无需复杂的本地安装和配置。 * **实际应用拓展：** 学习如何从博客和 RSS 订阅中提取新闻，并使用 HTML 可视化摘要结果。 * **进阶库应用：** 介绍并实践使用现有的文本摘要库，如 sumy、pysummarization 和 BERT summarizer。 * **实操导向：** 通过从零开始实现算法，帮助学习者掌握创建自定义摘要算法所需的全部知识。 **适合人群：** * 对文本摘要感兴趣的初学者。 * 希望巩固和复习文本摘要概念的进阶学习者。 通过本课程，您将全面掌握文本摘要的理论知识和实践技能，能够独立开发属于自己的文本摘要工具。
The area of Natural Language Processing (NLP) is a subarea of Artificial Intelligence that aims to make computers capable of understanding human language, both written and spoken. Some examples of practical applications are: translators between languages, translation from text to speech or speech to text, chatbots, automatic question and answer systems (Q & A), automatic generation of descriptions for images, generation of subtitles in videos, classification of sentiments in sentences, among many others! Another important application is the automatic document summarization, which consists of generating text summaries. Suppose you need to read an article with 50 pages, however, you do not have enough time to read the full text. In that case, you can use a summary algorithm to generate a summary of this article. The size of this summary can be adjusted: you can transform 50 pages into only 20 pages that contain only the most important parts of the text!Based on this, this course presents the theory and mainly the practical implementation of three text summarization algorithms: (i) frequency-based, (ii) distance-based (cosine similarity with Pagerank) and (iii) the famous and classic Luhn algorithm, which was one of the first efforts in this area. During the lectures, we will implement each of these algorithms step by step using modern technologies, such as the Python programming language, the NLTK (Natural Language Toolkit) and spaCy libraries and Google Colab, which will ensure that you will have no problems with installations or configurations of software on your local machine.In addition to implementing the algorithms, you will also learn how to extract news from blogs and the feeds, as well as generate interesting views of the summaries using HTML! After implementing the algorithms from scratch, you have an additional module in which you can use specific libraries to summarize documents, such as: sumy, pysummarization and BERT summarizer. At the end of the course, you will know everything you need to create your own summary algorithms! If you have never heard about text summarization, this course is for you! On the other hand, if you are already experienced, you can use this course to review the concepts.
