# Introduction to Automatic Text Summarization Automatic text summarization (ATS) is the process of creating a condensed version of a given text document. ATS aims to capture the main points of the text and produce a summary that is shorter than the original. The goal of ATS is to produce summaries that are both accurate and concise. There are a variety of methods that can be used to create automatic text summaries. These range from simple statistical approaches to more complex natural language processing (NLP) approaches. Each method has its own strengths and weaknesses, and thus a combination of methods may be used to produce the best results. In this article, we will discuss some of the most effective methods for creating more efficient automatic text summarization techniques. We will also discuss how these techniques can be evaluated and improved upon. Finally, we will look at how the application of these techniques can benefit organizations and individuals.

**Algorithm name:** TextRank **Description:** TextRank is an unsupervised algorithm used for automatic text summarization which works in a similar way to PageRank, a popular algorithm used by Google for ranking webpages. TextRank works by creating a graph of words in the text, with edges connecting words that appear in close proximity to each other. Word weights are then calculated based on the number of connections each word has, and the most important words are used to generate a summary of the text. **Pseudocode:** 1. Tokenize the text (split into individual words) 2. Create a graph of the words, with edges connecting words that appear in close proximity to each other 3. Calculate the weight of each word, based on the number of connections it has 4. Sort the words by weight 5. Select the top N most important words 6. Generate a summary by combining the words in meaningful phrases **Sample code:** ``` # Tokenize text tokens = nltk.word_tokenize(text) # Create graph of words graph = nx.Graph() for i in range(len(tokens) - 1): graph.add_edge(tokens[i], tokens[i+1]) # Calculate word weights weights = nx.pagerank(graph) # Sort weights sorted_weights = sorted(weights.items(), key=operator.itemgetter(1), reverse=True) # Select top N words top_words = [w for w,_ in sorted_weights[:N]] # Generate summary summary = ' '.join(top_words) ```