3__Python - Tool Text summarization.pptx
- 2. Text summarization in Natural Language Processing • Is a process of condensing and presenting the essential information from a source text while retaining its key ideas and meaning. • There are two main types of text summarization: extractive and abstractive 1. Extractive Summarization • Selects and extracts sentences or phrases directly from the source text. • Uses ranking algorithms to identify the most important sentences. • Typically relies on sentence scoring based on features like sentence length, word frequency, and position in the document. • Preserves the original wording of the selected sentences. 2. Abstractive Summarization: • Generates a summary by paraphrasing and rephrasing the content, often using natural language generation techniques. • Requires a deeper understanding of the content and context. • Involves the creation of new sentences that convey the main ideas of the source text. • Can potentially produce more concise and coherent summaries.
- 3. Components and Techniques: • a. Sentence Extraction: o Identifying the most important sentences using features like term frequency, sentence position, and more. • b. Sentence Compression: o Reducing the length of sentences while preserving the core information. • c. Named Entity Recognition (NER): o Identifying and preserving important entities in the summary. • d. Coreference Resolution: o Ensuring consistent references to entities and concepts throughout the summary.
- 4. Code example in python
- 5. Continuation of above code • # Example text for processing • input_text = """ • Natural language processing (NLP) is a subfield of artificial intelligence (AI) that focuses on the interaction between computers and humans through natural language. NLP techniques enable computers to interpret, understand, and generate human language in a meaningful way. • Sentence compression is a technique that involves shortening sentences while retaining their core meaning. It helps in creating more concise and focused content. • Named Entity Recognition (NER) is a process of identifying and classifying entities such as names of people, locations, organizations, and more in a given text. Coreference resolution is the task of determining when two or more expressions in a text refer to the same entity. It helps in understanding the relationships between different parts of the text. In conclusion, NLP techniques like sentence compression, NER, and coreference resolution contribute to the efficiency and understanding of natural language for various applications. """
- 7. Challenges in Text Summarization 1. Ambiguity: Resolving ambiguity in language, where words or phrases may have multiple meanings. 2. Coherence: Ensuring that the summary maintains a coherent and logical flow of ideas. 3. Information Loss: Balancing the reduction of information with the retention of key concepts. 4. Handling Diverse Content: Summarizing content from various domains and languages.
- 8. Applications of Text Summarization • a. News Articles: • Generating concise summaries of news articles for quick information retrieval. • b. Legal Documents: • Extracting key points from lengthy legal documents for efficient review. • c. Academic Papers: • Summarizing research papers to quickly understand the main contributions. • d. Conversational Agents: • Providing succinct responses in chatbots or virtual assistants. • e. Document Summarization: • Condensing lengthy documents for easier comprehension.
- 9. Popular Text Summarization Models • . Extractive: o Latent Semantic Analysis (LSA): Applies singular value decomposition to identify sentence relationships. o TextRank: Uses a graph-based ranking algorithm to identify key sentences. o BERTSUM: Adapts BERT (Bidirectional Encoder Representations from Transformers) for extractive summarization. • Abstractive: o Pointer-Generator Networks: Combines extractive and abstractive approaches by pointing to words in the source text. • Transformer-based Models: Models like GPT-3 and T5 have demonstrated strong performance in abstractive summarization.
- 10. Evaluation Metrics • ROUGE (Recall-Oriented Understudy for Gisting Evaluation): • Measures overlap in n-grams (unigrams, bigrams, etc.) between the generated summary and reference summaries. • BLEU (Bilingual Evaluation Understudy): • Measures the precision of n-grams in the generated summary compared to reference summaries. • Future Directions: • a. Multimodal Summarization: • Integrating information from text, images, and other modalities for more comprehensive summaries. • b. Context-aware Summarization: • Considering broader contextual information to generate more informed summaries. • Text summarization in NLP continues to be an active area of research and development, with ongoing efforts to improve the quality, coherence, and adaptability of automated summarization systems across various domains and languages.