Text Summarization

Published Paper

This document explains multiple approaches to text summarization, combining frequency-based methods, position-based methods, TF-IDF-based scoring, and transformer-based models (HuggingFace Transformers).

The goal of summarization is to condense a longer body of text into its most important parts while retaining meaning.

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Cleaning and Preprocessing Text

Before applying summarization techniques, the text needs to be cleaned:

• Remove special characters and digits.
• Tokenize text into words and sentences.
• Lemmatize words using WordNetLemmatizer.
• Remove stopwords to reduce noise.

The cleaned text is then used in different summarization algorithms.

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Frequency-Based Summarization

Approach:

• Tokenize text into sentences.
• Count word frequency after removing stopwords.
• Normalize frequency values by dividing by the maximum frequency.
• Score each sentence based on word frequencies.
• Select the top-N sentences as the summary.

Position-Based Summarization

Approach:

• Tokenize text into sentences.
• Score sentences based on their position in the text (earlier sentences often carry more importance).
• Select the top-N sentences by position score.

TF-IDF Based Summarization

Approach:

• Create a frequency matrix of words in each sentence.
• Compute Term Frequency (TF) and Inverse Document Frequency (IDF).
• Calculate TF-IDF scores for words.
• Score sentences by averaging TF-IDF values of words.
• Select the best sentences above a set threshold.

This method ensures that rare but important words have higher weight in the summary.

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Sample Case

Input: Peter and Elizabeth took a taxi to attend the night party in the city. While in the party, Elizabeth collapsed and WAS rushed to the hospital.

Position-based summary:

Peter Elizabeth took taxi attend night party city party Elizabeth collapsed rushed hospital

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Transformer-Based Summarization (HuggingFace)

Approach:

• Uses pre-trained deep learning models such as sshleifer/distilbart-cnn-12-6.
• Capable of generating abstractive summaries, unlike extractive methods.
• Requires the transformers library and a summarization pipeline.

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Key Takeaways

1. Frequency-based summarization selects sentences with the most frequent important words.
2. Position-based summarization prioritizes sentences based on placement in the document.
3. TF-IDF summarization balances word frequency with uniqueness, highlighting rare but meaningful terms.
4. Transformer-based summarization (e.g., DistilBART) provides abstractive summaries, rephrasing content instead of just extracting sentences.

Each approach has strengths and trade-offs:

• Rule-based methods (frequency, TF-IDF, position) are fast and simple.
• Transformer-based methods are more accurate but computationally expensive.

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Confirmation Letter with Google Scholar Impact Factor