1. Course overview Introduction to summarization Lecture 1

2. Instructor: Ani Nenkova – 505 Levine, nenkova@seas.upenn.edunenkova@seas.upenn.edu – Office hours: Tuesdays 3:154:15 or by appointment TA: Annie Louis – lannie@seas.upenn.edu lannie@seas.upenn.edu

3. Textbook No required text – Slides/lecture notes and handouts will be given in class Recommended – Speech and Language Processing (second edition, 2007, Prentice-Hall), by Daniel Jurafsky and James Martin Speech and Language Processing Also see – Christopher Manning and Hinrich Schutze, Foundations of statistical natural language processing – Advances in Automatic Text Summarization Edited by Inderjeet Mani and Mark T. MayburyInderjeet ManiMark T. Maybury

4. Grading 5 homeworks (65%) – One will be a literature overview assignment – One will be at the end of the semester, instead of a final You are encouraged to form teams for the homework (programming) assignments, but all write-ups should be individual Midterm (20%) Class participation (15%) – Submit 5 questions each week

5. Late submission policy 5 late days for the semester – Can be used for any assignment with no penalty Late submissions after late days have been used up will not be graded

6. What you will learn A lot about summarization and natural language techniques used in summarization Tools and resources – Part of speech and named entity taggers, parsers, Wordnet, WEKA

7. Problem formalization/distributions – Distributions: Zipfian, Binomial, Multinomial – Graph representations System comparisons – Statistical significance and statistical tests

8. Reading scientific articles – Part of the assigned readings – Useful skill, regardless of your future job plans Improving writing skills – Immensely useful, regardless of your future job plans – The literature overview assignment will focus on this, but in other assignments the way you describe your work will also be evaluated

9. What is summarization?

11. Columbia Newsblaster The academic version

12. What is the input? News, or clusters of news – a single article or several articles on a related topic Email and email thread Scientific articles Health information: patients and doctors Meeting summarization Video

13. What is the output Keywords Highlight information in the input Chunks or speech directly from the input or paraphrase and aggregate the input in novel ways Modality: text, speech, video, graphics

14. Ideal stages of summarization Analysis – Input representation and understanding Transformation – Selecting important content Realization – Generating novel text corresponding to the gist of the input

15. Most current systems Use shallow analysis methods – Rather than full understanding Work by sentence selection – Identify important sentences and piece them together to form a summary

16. Data-driven approaches Relying on features of the input documents that can be easily computes from statistical analysis Word statistics Cue phrases Section headers Sentence position

17. Knowledge-based systems Use more sophisticated natural language processing Discourse information – Resolve anaphora, text structure Use external lexical resources – Wordnet, adjective polarity lists, opinion Using machine learning

18. What are summaries useful for? Relevance judgments – Does this document contain information I am interested in? – Is this document worth reading? Save time Reduce the need to consult the full document

19. Multi-document summarization Very useful for presenting and organizing search results – Many results are very similar, and grouping closely related documents helps cover more event facets – Summarizing similarities and differences between documents

20. Scientific article summarization Not only what the article is about, but also how it relates to work it cites Determine which approaches are criticized and which are supported – Automatic genre specific summaries are more useful than original paper abstracts

21. Other uses Document indexing for information retrieval Automatic essay grading, topic identification module

22. Data-driven summarization

23. Frequency as indicator of importance The topic of a document will be repeated many times In multi-document summarization, important content is repeated in different sources

24. Greedy frequency method Compute word probability from input Compute sentence weight as function of word probability Pick best sentence

25. How to deal with redundancy? Author JK Rowling has won her legal battle in a New York court to get an unofficial Harry Potter encyclopaedia banned from publication. A U.S. federal judge in Manhattan has sided with author J.K. Rowling and ruled against the publication of a Harry Potter encyclopedia created by a fan of the book series. – Shallow techniques not likely to work well

26. Global optimization for content selection What is the best summary? vs What is the best sentence? Form all summaries and choose the best – What is the problem with this approach?

27. Sentence clustering for theme identification 1. PAL was devastated by a pilots' strike in June and by the region's currency crisis. 2. In June, PAL was embroiled in a crippling three- week pilots' strike. 3. Tan wants to retain the 200 pilots because they stood by him when the majority of PAL's pilots staged a devastating strike in June.

28. Cluster sentences from the input into similar themes Choose one sentence to represent a theme Consider bigger themes as more important

29. Using graph representations Nodes – Sentences – Discourse entities Arcs – Between similar sentences – Between related entities

30. Using machine learning Ask people to select sentences Use these as training examples for machine learning – Each sentence is represented as a number of features – Based on the features distinguish sentences that are appropriate for a summary and sentences that are not Run on new inputs

31. Information ordering In what order to present the selected sentences? – An article with permuted sentences will not be easy to understand Very important for multi-document summarization – Sentences coming from different documents

32. Automatic summary edits Some expressions might not be appropriate in the new context – References: – he – Putin – Russian Prime Minister Vladimir Putin – Discourse connectives However, moreover, subsequently Requires more sophisticated NLP techniques

33. Before Pinochet was placed under arrest in London Friday by British police acting on a warrant issued by a Spanish judge. Pinochet has immunity from prosecution in Chile as a senator-for-life under a new constitution that his government crafted. Pinochet was detained in the London clinic while recovering from back surgery.

34. After Gen. Augusto Pinochet, the former Chilean dictator, was placed under arrest in London Friday by British police acting on a warrant issued by a Spanish judge. Pinochet has immunity from prosecution in Chile as a senator-for-life under a new constitution that his government crafted. Pinochet was detained in the London clinic while recovering from back surgery.

35. Before Turkey has been trying to form a new government since a coalition government led by Yilmaz collapsed last month over allegations that he rigged the sale of a bank. Ecevit refused even to consult with the leader of the Virtue Party during his efforts to form a government. Ecevit must now try to build a government. Demirel consulted Turkey's party leaders immediately after Ecevit gave up.

36. After Turkey has been trying to form a new government since a coalition government led by Prime Minister Mesut Yilmaz collapsed last month over allegations that he rigged the sale of a bank. Premier-designate Bulent Ecevit refused even to consult with the leader of the Virtue Party during his efforts to form a government. Ecevit must now try to build a government. President Suleyman Demirel consulted Turkey's party leaders immediately after Ecevit gave up.