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Text summarization
This document discusses text summarization using machine learning. It begins by defining text summarization as reducing a text to create a summary that retains the most important points. There are two main types: single document summarization and multiple document summarization. Extractive summarization creates summaries by extracting phrases or sentences from the source text, while abstractive summarization expresses ideas using different words. Supervised machine learning approaches use labeled training data to train classifiers to select content, while unsupervised approaches select content based on metrics like term frequency-inverse document frequency. ROUGE is commonly used to automatically evaluate summaries by comparing them to human references. Query-focused multi-document summarization aims to answer a user's information need by summarizing relevant documents
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Text summarization
- 1. Text Summarization -Machine Learning TEXT SUMMARIZATION 1 Kareem El-Sayed Hashem Mohamed Mohsen Brary
- 2. TEXT SUMMARIZATION Goal: reducing a text with a computer program in order to create a summary that retains the most important points of the original text. Text Summarization - Machine Learning Summarization Applications summaries of email threads action items from a meeting simplifying text by compressing sentences 2
- 3. WHAT TO SUMMARIZE? SINGLEVS. MULTIPLE DOCUMENTS Single Document Summarization Given a single document produce Text Summarization - Machine Learning Abstract Outline Headline Multiple Document Summarization Given a group of document produce a gist of the document A series of news stories of the same event A set of webpages about some topic or question 3
- 4. QUERY-FOCUSED SUMMARIZATION & GENERICSUMMARIZATION Generic Summarization Summarize the content of a document Text Summarization - Machine Learning Query-focused Summarization Summarize a document with respect to an information need expressed in a user query A kind of complex question answering Answer a question by summarizing a document that has the information to construct the answer 4
- 5. SUMMARIZATION FOR QUESTION ANSWERING: Snippets Create snippets summarizing a web page for a query Text Summarization - Machine Learning Multiple Documents Create answer to complex questions summarizing multiple documents. Instead of giving a snippet for each document Create a cohesive answer that combines information from each document 5
- 6. EXTRACTIVE SUMMARIZATION & ABSTRACTIVESUMMARIZATION Extractive Summarization: Create the summary from phrases or sentences in the source document(s) Text Summarization - Machine Learning Abstractive Summarization Express the ideas in the source document using different words 6
- 7. SUMMARIZATION: THREE STAGES Content Selection: choose sentences to extract from the document Text Summarization - Machine Learning Information Ordering: choose an order to place them in the summary Sentence Realization: clean up the sentence 7
- 8. UNSUPERVISED CONTENT SELECTION Intuition Dating Back to Luhn (1958): Choose sentences that have distinguished or informative words Text Summarization - Machine Learning Two Approaches to Define distinguished words tf-idf: weigh each word wi in document j by tf-idf Topic signature: choose smaller set of distinguished words Log-likelihood ratio (LLR) 8
- 9. TOPIC SIGNATURE-BASED CONTENT SELECTIONWITH QUERIES Choose words that are informative either By log-likelihood ratio (LLR) Text Summarization - Machine Learning Or by appearing in the query Weigh a sentence by weight of its words: 9
- 10. SUPERVISED CONTENT SELECTION Given A labeled training set of good summaries for each document Text Summarization - Machine Learning Align The sentences in the document with sentences in the summary Extract Features Position Length of sentence Word informativeness Cohesion 10
- 11. SUPERVISED CONTENT SELECTION Train A binary classifier (put sentence in summary? Yes or no) Text Summarization - Machine Learning Problems Hard to get labeled training data Alignment is difficult Performance not better that unsupervised algorithm 11
- 12. EVALUATING SUMMARIES: ROUGE ROUGE “ Recall Oriented Understudy for Gisting Evaluation ” Text Summarization - Machine Learning Internal metric for automatically evaluating summaries Based on BLEU (a metric used for machine translation) Not as good as human evaluation. But much more convenient 12
- 13. EVALUATING SUMMARIES: ROUGE Given a document D, and an automatic summary X: Text Summarization - Machine Learning Have N humans produce a set of reference summaries of D Run System, giving automatic summary X What percentage of the bigrams from the reference summaries appear in X? 13
- 14. EXAMPLE Human 1:water spinach is a green leafy vegetable grown in the tropics. Text Summarization - Machine Learning Human 2: water spinach is a semi-aquatic tropical plant grown as a vegetable. Human 3: water spinach is a commonly eaten leaf vegetable of Asia. System: water spinach is a leaf vegetable commonly eaten in tropical areas of Asia. ROUGE -2= = 12/28 = 0.43 14
- 15. ANSWERING HARDER QUESTION: QUERY-FOCUSEDMULTI-DOCUMENT SUMMARIZATION The (bottom-up) snippet method Find a set of relevant documents Text Summarization - Machine Learning Extract informative sentences form the documents Order and modify the sentences into an answer The(top-down) information extraction method Build specific answers for different questions types: Definition questions Biography questions Certain medical questions 15
- 16. QUERY-FOCUSED MULTI-DOCUMENT SUMMARIZATION Text Summarization - Machine Learning 16
- 17. MAXIMAL MARGINAL RELEVANCE(MMR) An iterative method for content selection from multiple documents Text Summarization - Machine Learning Iteratively (greedily) choose the best sentence to insert in the summary/answer so far: Relevant: maximally relevant to the user query High cosine similarity to the query Novel: minimally redundant with the summary so far: Low cosine similarity to the summary 17 Stop when desired length
- 18. LLR + MMRCHOOSING INFORMATIVE YET NON-REDUNDANT SENTENCES One of many ways to combine the intuitions of LLR and MMR: Text Summarization - Machine Learning Score each sentence based on LLR (including query words) Include the sentence with highest score in the summary Iteratively add into the summary high-scoring sentences that are not redundant with the summary so far. 18
- 19. INFORMATION ORDERING Chronological ordering: Order sentences by the date of the document “ for summarizing news” Text Summarization - Machine Learning Coherence: Choose ordering that make neighboring sentences similar(by cosine similarity) Choose ordering in which neighboring sentences discuss the same entity Topical ordering 19 Learn the ordering of topics in the source document
- 20. DOMAIN-SPECIFIC ANSWERING: THE INFORMATIONEXTRACTION METHOD A good biography of a person contains: A person’s birth/death, fame factor, education …etc Text Summarization - Machine Learning A good definition contains Type or category “ The Hajj is a type of ritual ” A medical answer about a drug’s use contains: The problem : medical condition The intervention : drug or procedure The outcome : the result of the study 20
- 21. INFORMATION THAT SHOULDBE IN THE ANSWER FOR 3 KINDS OF QUESTIONS Text Summarization - Machine Learning 21
- 22. ARCHITECTURE FOR ANSWERINGCOMPLEX QUESTIONS Text Summarization - Machine Learning 22
- 23. Text Summarization -Machine Learning 23 NLP Stanford course. REFERENCES:
- 24. Text Summarization -Machine Learning THANK YOU 24