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Course overview Introduction to summarization . Lecture 1. Instructor: Ani Nenkova 505 Levine, nenkova@seas.upenn.edu Office hours: Tuesdays 3:15—4:15 or by appointment TA: Annie Louis lannie@seas.upenn.edu. Textbook. No required text

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slide2
Instructor: Ani Nenkova
    • 505 Levine, nenkova@seas.upenn.edu
    • Office hours: Tuesdays 3:15—4:15 or by appointment
  • TA: Annie Louis
    • lannie@seas.upenn.edu
textbook
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
  • Also see
    • Christopher Manning and Hinrich Schutze, “Foundations of statistical natural language processing”
    • Advances in Automatic Text SummarizationEdited by Inderjeet Mani and Mark T. Maybury
grading
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
late submission policy
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
what you will learn
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
slide7
Problem formalization/distributions
    • Distributions: Zipfian, Binomial, Multinomial
    • Graph representations
  • System comparisons
    • Statistical significance and statistical tests
slide8
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
columbia newsblaster
Columbia Newsblaster
  • The academic version
what is the input
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
what is the output
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
ideal stages of summarization
Ideal stages of summarization
  • Analysis
    • Input representation and understanding
  • Transformation
    • Selecting important content
  • Realization
    • Generating novel text corresponding to the gist of the input
most current systems
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
data driven approaches
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
knowledge based systems
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
what are summaries useful for
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
multi document summarization
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
scientific article summarization
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
other uses
Other uses
  • Document indexing for information retrieval
  • Automatic essay grading, topic identification module
frequency as indicator of importance
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
greedy frequency method
Greedy frequency method
  • Compute word probability from input
  • Compute sentence weight as function of word probability
  • Pick best sentence
how to deal with redundancy
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
global optimization for content selection
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?
sentence clustering for theme identification
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.

slide28
Cluster sentences from the input into similar themes
  • Choose one sentence to represent a theme
  • Consider bigger themes as more important
using graph representations
Using graph representations
  • Nodes
    • Sentences
    • Discourse entities
  • Arcs
    • Between similar sentences
    • Between related entities
using machine learning
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
information ordering
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
automatic summary edits
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
before
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.

after
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.

before35
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.

after36
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.