This document discusses text summarization, highlighting its importance and the natural language processing (NLP) techniques that automate the process, specifically focusing on extractive summarization. Key stages include text preprocessing, sentence scoring using various techniques like tf-idf and textrank, and selecting important sentences to create concise summaries. The document also covers limitations of extractive summarization and the development of hybrid approaches that integrate both extractive and abstractive methods for improved accuracy.

1. NLP Techniques for Text Summarization
Section 1: Introduction
Text summarization is the process of creating a shorter version of a longer text while retaining
the most important information. It can be done manually or using automated techniques. With
the exponential growth of the internet and the amount of information available, text
summarization has become increasingly important. Natural Language Processing (NLP)
techniques have been developed to automate the process of text summarization. In this article,
we will explore some of the most common NLP techniques used for text summarization.
Before we dive into the techniques, it is important to note that text summarization can be
categorized into two types: extractive and abstractive. Extractive summarization involves
selecting the most important sentences or phrases from the original text and combining them to
create a summary. Abstractive summarization, on the other hand, involves generating new
sentences that convey the most important information from the original text. In this article, we
will focus on extractive summarization.
Let's get started.
Section 2: Text Preprocessing
Before we can apply any NLP technique to a text, we need to preprocess it. Text preprocessing
involves cleaning and transforming the raw text into a format that can be easily analyzed. The
following techniques are commonly used for text preprocessing:
1. Tokenization: Breaking down the text into individual words or phrases (tokens).
2. Stopword removal: Removing common words that do not carry much meaning, such as "and",
"the", "a".
3. Stemming: Reducing words to their root form. For example, "running" and "ran" would be
stemmed to "run".
Section 3: Sentence Scoring
Once the text has been preprocessed, we can move on to scoring each sentence. The goal is to
identify the most important sentences that should be included in the summary. The following
techniques are commonly used for sentence scoring:
1. Term frequency-inverse document frequency (TF-IDF): This technique assigns a score to each
sentence based on the frequency of the words it contains and how rare those words are in the
entire text.

2. 2. TextRank: This technique is based on PageRank, a link analysis algorithm used by Google to
rank web pages. TextRank assigns a score to each sentence based on the number of other
sentences that link to it.
3. Latent Semantic Analysis (LSA): This technique uses a mathematical model to identify the
underlying concepts in the text and assigns a score to each sentence based on how closely it
relates to those concepts.
Section 4: Sentence Selection
After scoring each sentence, we need to select the most important ones to include in the
summary. There are several ways to do this:
1. Threshold-based selection: We can set a threshold score and only include sentences that
exceed that score.
2. Top N selection: We can simply select the top N highest scoring sentences to include in the
summary.
3. Clustering: We can group similar sentences together and select one representative sentence
from each cluster to include in the summary.
Section 5: Text Compression
Once we have selected the most important sentences, we can further compress the text to create a
shorter summary. The following techniques are commonly used for text compression:
1. Sentence fusion: Combining two or more sentences to create a single sentence that conveys
the same information.
2. Sentence splitting: Splitting a long sentence into two or more shorter sentences.
3. Word substitution: Replacing longer words with shorter synonyms.
Section 6: Evaluation Metrics
In order to evaluate the effectiveness of our summarization techniques, we need to use evaluation
metrics. The following metrics are commonly used:
1. ROUGE: Measures the overlap between the generated summary and the reference summary
(i.e. the "gold standard" summary created by a human).
2. BLEU: Measures the n-gram overlap between the generated summary and the reference
summary.

3. 3. F1 score: A weighted average of precision and recall, which measures how well the generated
summary matches the reference summary.
Section 7: Limitations of Extractive Summarization
While extractive summarization can be effective, it also has its limitations. One major limitation
is that it can only summarize what is already present in the text. It cannot generate new
information or insights. Additionally, extractive summarization may not capture the overall
meaning or tone of the text, as it only selects individual sentences.
Section 8: Hybrid Approaches
To overcome the limitations of extractive summarization, researchers have developed hybrid
approaches that combine extractive and abstractive techniques. These approaches aim to generate
more accurate and informative summaries. One such approach is the transformer-based model,
which has been shown to outperform other techniques on various datasets.
Section 9: Applications of Text Summarization
Text summarization has a wide range of applications, including:
1. News summarization: Creating a shortened version of news articles for quick consumption.
2. Legal document summarization: Summarizing lengthy legal documents for faster analysis.
3. Social media summarization: Summarizing social media posts for sentiment analysis.
4. Email summarization: Summarizing long email threads for easy understanding.
Section 10: Conclusion
NLP techniques have made significant strides in automating the process of text summarization.
While extractive summarization has its limitations, it can still be an effective way to create a
shorter version of a longer text. By preprocessing the text, scoring each sentence, selecting the
most important ones, and compressing the text, we can create a summary that conveys the most
important information. Hybrid approaches that combine extractive and abstractive techniques are
also promising for generating more accurate and informative summaries. With the increasing
amount of information available, text summarization will only become more important in the
future.