COURSE INFORMATION
Instructor	Ani Nenkova, nenkova@seas.upenn.edu Office: Levine Hall 505 Office Hours: Tuesday 3:15 to 4:15, or by appointment
Time	TT 4:30-6pm
Location	Moore 212
Description	Automatic summarization as part of information retrieval systems can help alleviate the information overload problem caused by the unprecedented amount of online textual information. The building of a summarization system requires good understanding of the properties of human language and the use of various natural language tools. In this course we will build several summarization systems of increasing complexity and sophistication. In the process we will learn about various natural language processing tools and resources such as part of speech tagging, chunking, parsing, Wordnet, and machine learning toolkits, and will overview the fundamentals of information retieval systems. We will also cover probability and statistics concepts used in summarization, but also applicable to a wide range of other language-related and information retrieval tasks. Topics to be covered include: Introduction to summarization Applications of summarization, data-driven methods, supervised summarization Word distribution and weigting schemes Word frequency, Zipffs law, stopwords, tf.idf, entropy Statistics concepts probabilities, binomial and multinomial distribution, log-likelihood ratio statistics Language processing tools part-of-speech tagging, chunking, parsing and language resources Supervised summarization Getting data (human models, summary-input alignment), training a classifier (feature extraction, using WEKA), classifier performance measures (accuracy, precision, recall) System performance evaluation and comparison Introduction to R, correlation coefficients, p-values, tests for statistical significance Discussion of assigned technical papers Discussions will be held throughout the semester
Textbook	There is no required textbook for the class. However, here are two texts that you will find useful and interesting if you decide you want to further persue some of the topics. SPEECH and LANGUAGE PROCESSING: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Second Edition, Daniel Jurafsky and James H. Martin, Prentice Hall Series in Artificial Intelligence, 2008. Introduction to Information Retrieval , Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Cambridge University Press. 2008.
Grading	5 homeworks (65% total) One will be with focus on clear writing on complex topics---literature overview You are encouraged to work in teams, but the write-ups should be individual 5 late days for the semester Midterm (20%) Class participation (15%)

Date	Topic and Readings
Sep 4	Very brief class intro; no real class because of conflict with CIS400
Sep 9	Course overview Introduction to summarization and language applications .ppt
Sep 11	Vocabulary size and term distribution: tokenization, text normalization, stemming Reading: Chapter 23 from J-M textbook, Question Answering and Summarization .ppt
Sep 16	Term weighting and vector represenation of text .ppt
Sep 18	Language models; Evaluation in information retrieval Reading: Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status .ppt
Sep 23	Approaches to automatic summarization .ppt
Sep 25	Probability theory; Bayes Theoren and Naive Bayes classification Reading: How to Write a Spelling Corrector HW1 out .ppt
Sep 29	Text categorization and feature selection; chi square test .ppt
Oct 2	Homework discussion; Exercises on text representations, calculating probabilities, similarities, etc; Discussion of readings Reading: (1) A trainable document summarizer (2) Language Identification: Examining the Issues
Oct 6	Measures of association: chi square test, mutual information, binomial distribution and log likelihood ratio .ppt
Oct 9	Part of speech tagging HW1 due Readings: (1) Chapter 5, M&S (2) Experiments in multi-document summarization .ppt
Oct 14	No class; fall break
Oct 16	Homework discussion; Log likelihood ratio test and topic signature words HW2 Part1 out Readings: (1) Topic-Focused Multi-document Summarization Using an Approximate Oracle Score (2) The Automated Acquisition of Topic Signatures for Text Summarization .ppt
Oct 21	Introduction to WordNet .ppt
Oct 23	Word sense disambiguation and word similarity Reading: Automatic record reviews .ppt
Oct 28	Word sense disambiguation
Oct 30	Discussion of assigned readings .ppt
Nov 4	Lexical chains for summarization Readings: Using lexical chains for text summarization Efficiently computed lexical chains as an intermediate representation for automatic text summarization
Nov 6	Web search .ppt HW3 and take home midterm distributed
Nov 11	Readings: Entropy of search logs A taxonomy of web search
Nov 14	Discourse, coheremce and anaphora resolution .ppt
Nov 18	Evaluation in summarization; Summarization beyond extraction .ppt Reading: Summarization Evaluation for Text and Speech
Nov 20	Discussion of midterm; writing
Nov 25	Randomized tests for statistical significance
Dec 2	Predicting input difficulty; Evaluation without human models .ppt Reading: Identifying correlates of input difficulty for generic multi-document summarization
Dec 4	Final review Reading: Opinion mining and sentiment analysis