course overview introduction to summarization lecture 1
Course overviewIntroduction to summarization
Instructor: Ani Nenkova– 505 Levine, [email protected]– Office hours: Tuesdays 3:15—4:15 or by
TA: Annie Louis– [email protected]
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 Summarization
Edited by Inderjeet Mani and Mark T. Maybury
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
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
A lot about summarization and natural language techniques used in summarization
Tools and resources– Part of speech and named entity taggers, parsers,
Problem formalization/distributions– Distributions: Zipfian, Binomial, Multinomial– Graph representations
System comparisons– Statistical significance and statistical tests
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
What is summarization?
The academic version
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
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
Analysis– Input representation and understanding
Transformation– Selecting important content
Realization– Generating novel text corresponding to the gist of the input
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
Relying on features of the input documents that can be easily computes from statistical analysis
Word statistics Cue phrases Section headers Sentence position
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?
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
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
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
Document indexing for information retrieval
Automatic essay grading, topic identification module
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
Compute word probability from input
Compute sentence weight as function of word probability
Pick best sentence
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
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
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.
Cluster sentences from the input into similar themes
Choose one sentence to represent a theme
Consider bigger themes as more important
Using graph representations
Nodes– Sentences– Discourse entities
Arcs– Between similar sentences– Between related entities
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
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
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
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.
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.
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.
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.