processing of large document collections part 3. text summarization z”process of distilling the...

Processing of large document collections

Part 3

Text summarization

”Process of distilling the most important information from a source to produce an abridged version for a particular user or task”

Text summarization

Many everyday uses: headlines (from around the world) outlines (notes for students) minutes (of a meeting) reviews (of books, movies) ...

Architecture of a text summarization system

Input: a single document or multiple

documents text, images, audio, video database


output: extract or abstract compression rate

ratio of summary length to source length

connected text or fragmentary generic or user-focused/domain-specific indicative or informative


Three phases: analyzing the input text transforming it into a summary

representation synthesizing an appropriate output form

Condensation operations

Selection of more salient or non-redundant information

aggregation of information (e.g. from different parts of the source, or of different linguistic descriptions)

generalization of specific information with more general, abstract information

The level of processing

Surface levelentity leveldiscourse level

Surface-level approaches

Tend to represent information in terms of shallow features

the features are then selectively combined together to yield a salience function used to extract information

Surface level

Shallow features thematic features

presence of statistically salient terms, based on term frequency statistics

locationposition in text, position in paragraph, section

depth, particular sections

backgroundpresence of terms from the title or headings in

the text, user’s query

Surface level

Cue words and phrases ”in summary”, ”our investigation” emphasizers like ”important”, ”in

particular” domain-specific bonus (+ ) and stigma

(-) terms

Entity-level approaches

Build an internal representation for text

modeling text entities and their relationships

tend to represent patterns of connectivity in the text to help determine what is salient

Relationships between entities

Similarity (e.g. vocabulary overlap)proximity (distance between text units)co-occurrence (words related based on

their occurring in common contexts)thesaural relationships among words

(synonymy, hypernymy, part-of relations)co-reference (of referring expressions

such as noun phrases)

Relationships between entities

Logical relationships (agreement, contradiction, entailment, consistency)

syntactic relations (based on parse trees)

meaning representation-based relations (e.g. based on predicate-argument relations)

Discourse-level approaches

Model the global structure of the text and its relation to communicative goals

structure can include: format of the document (e.g. hypertext

markup) threads of topics as they are revealed in

the text rhetorical structure of the text, such as

argumentation or narrative structure

Classical approaches

Luhn ’58Edmundson ’69

Luhn’s method

Filter terms in the document using a stoplist

Terms are normalized based on aggregating together ortographically similar terms

Frequencies of aggregated terms are calculated and non-frequent terms are removed

Luhn’s method

Sentences are weighted using the resulting set of ”significant” terms and a term density measure

each sentence is divided into segments bracketed by significant terms not more than 4 non-significant terms apart

Luhn’s method

each segment is scored by taking the square of the number of bracketed significant terms divided by the total number of bracketed terms

the score of the highest scoring segment is taken as the sentence score

the highest scoring sentences are chosen to the summary

Edmundson’s method

Extends earlier work to look at three features in addition to word frequencies: cue phrases (e.g. ”significant”,

”impossible”, ”hardly”) title and heading words location


Programs to weight sentences based on each of the four methods separately

programs were evaluated by comparison against manually created extracts

corpus-based methodology: training set and test set in the training phase, weights were

manually readjusted


Results: three additional features dominated

word frequency measures the combination of cue-title-location

was the best, with location being the best individual feature

keywords alone was the worst

Fundamental issues

What are the most powerful but also more general features to exploit for summarization?

How do we combine these features?How can we evaluate how well we

are doing?

Corpus-based approaches

In the classical methods, various features (thematic features, title, location, cue phrase) were used to determine the salience of information for summarization

an obvious issue: determine the relative contribution of different features to any given text summarization task


Contribution is dependent on the text genre, e.g. location: in newspaper stories, the leading text

often contains a summary in TV news, a preview segment may

contain a summary of the news to come in scientific text: an author-written

abstract


The importance of different text features for any given summarization problem can be determined by counting the occurrences of such features in text corpora

in particular, analysis of human-generated summaries, along with their full-text sources, can be used to learn rules for summarization


One could use a corpus to model particular components, without using a completely trainable approach e.g. a corpus can be used to compute

weights (TFIDF)


Challenges creating a suitable text corpus,

designing an annotation scheme ensuring the suitable set of summaries

is availablemay already be available: scientific papersif not: author, professional abstractor, judge

KPC method

Kupiec, Pedersen, Chen (1995): A Trainable Document Summarizer

a learning method using a corpus of abstracts written by professional human abstractors (Engineering Information Co.)

naïve Bayesian classification method is used

KPC method: features

Sentence-length cut-off feature given a threshold (e.g. 5 words), the feature

is true for all sentences longer than the threshold, and false otherwise

fixed-phrase feature this feature is true for sentences that contain

any of 26 indicator phrases (e.g. ”this letter…”, ”In conclusion…”), or that follow section head that contain specific keywords (e.g. ”results”, ”conclusion”)


Paragraph feature sentences in the first 10 paragraphs and

the last 5 paragraphs in a document get a higher value

in paragraphs: paragraph-initial, paragraph-final, paragraph-medial are distinguished


thematic word feature a small number of thematic words (the

most frequent content words) are selected each sentence is scored as a function of

frequency of the thematic words highest scoring sentences are selected binary feature: feature is true for a

sentence, if the sentence is present in the set of highest scoring sentences


Uppercase word feature proper names and explanatory text for

acronyms are usually important feature is computed like the thematic word

feature an uppercase thematic word

is not sentence-initial and begins with a capital letter and must occur several times

first occurrence is scored twice as much as later occurrences

KPC method: classifier

For each sentence, we compute the probability it will be included in a summary S given the k features Fj, j=1…k

the probability can be expressed using Bayes’ rule:

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KPC method: classifier

Assuming statistical independence of the features:

P(sS) is a constant, and P(Fj| sS) and P(Fj) can be estimated directly from the training set by counting occurrences

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KPC method: corpus

Corpus is acquired from Engineering Information Co, which provides abstracts of technical articles to online information services

articles do not have author-written abstracts

abstracts were created by professional abstractors

KPC method: corpus

188 document/summary pairs sampled from 21 publications in the scientific/technical domain

summaries are mainly indicative, average length is 3 sentences

average number of sentences in the original documents is 86

author, address, and bibliography were removed

KPC method: sentence matching

The abstracts from the human abstractors are not extracts but inspired by the original sentences

the automatic summarization task here: extract sentences that the human

abstractor might have chosen to prepare summary text (with minor modifications…)


For training, a correspondence between the manual summary sentences and sentences in the original document need to be obtained

matching can be done in several ways


matching can be done in several ways: a direct sentence match

the same sentence is found in both

a direct join 2 or more original sentences were used to

form a summary sentence

summary sentence can be ’unmatchable’ summary sentence (single or joined) can

be ’incomplete’


Matching was done in two passes first, the best one-to-one sentence

matches were found automatically (79%)

second, these matches were used as a starting point for the manual assignment of correspondences

KPC method: evaluation

Cross-validation strategy for evaluation documents from a given journal were

selected for testing one at a time; all other document/summary pairs were used for training

unmatchable and incomplete sentences were excluded

total of 498 unique sentences


Two ways of evaluation the fraction of manual summary sentences

that were faithfully reproduced by the summarizer programthe summarizer produced the same number of

sentences as were in the corresponding manual summary

-> 35% 83% is the highest possible value, since

unmatchable and incomplete sentences were excluded


The fraction of the matchable sentences that were correctly identified by the summarizer-> 42%

the effect of different features was also studiedbest combination (44%): paragraph, fixed-phrase,

sentence-lengthbaseline: selecting sentences from the beginning of

the document (result: 24%)

if 25% of the original sentences selected: 84%

Discourse-based approaches

Discourse structure appears to play an important role in the strategies used by human abstractors and in the structure of their abstracts

an abstract is not just a collection of sentences, but it has an internal structure -> abstract should be coherent and it should

represent some of the argumentation used in the source

Discourse models

Cohesion relations between words or referring

expressions, which determine how tightly connected the text isanaphora, ellipsis, synonymy, hypernymy (dog is-

a-kind animal)

coherence overall structure of a multi-sentence text in

terms of macro-level relations between sentences (e.g. ”although” -> contrast)

Boguraev, Kennedy (BG)

Goal: identify those phrasal units across the entire span of the document that best function as representative highlights of the document’s content

these phrasal units are called topic stamps

a set of topic stamps is called capsule overview

BG

A capsule overview not a set/sequence of sentences a semi-formal (normalised) representaion

of the document, derived after a process of data reduction over the original text

not always very readable, but still represents the flow of the narrativecan be combined with surrounding information

to produce more coherent presentation

BG

Primary consideration: methods should apply to any document type and source (domain independence)

also: efficient and scalable technology shallow syntactic analysis, no

comprehensive parsing engine needed

BGBased on the findings on technical terms

technical terms have such linguistic properties that can be used to find terms automatically in different domains quite reliably

technical terms seem to be topicaltask of content characterization

identifying phrasal units that have lexico-syntactic properties similar to technical terms discourse properties that signify their status as most

prominent

BG: terms as content indicators

Problems undergeneration overgeneration differentiation

Undergeneration

a set of phrases should contain an exhaustive description of all the entities that are discussed in the text

the set of technical terms has to be extended to include also expressions with pronouns etc.

Overgeneration

already the set of technical terms can be large

extensions make the information overload even worse

solution: phrases that refer to one participant in the discourse are combined with referential links

Differentiation

The same list of terms may be used to describe two documents, even if they, e.g., focus on different subtopics

it is necessary to differentiate term sets not only according to their membership, but also according to the relative representativeness of the terms they contain

Term sets and coreference classes

Phrases are extracted using a phrasal grammar (e.g. a noun with modifiers) also expressions with pronouns and

incomplete expressions are extracted using a (Lingsoft) tagger that provides

information about the part of speech, number, gender, and grammatical function of tokens in a text

solves the undergeneration problem

Term sets and coreference classes

The phrase set has to be reduced to solve the problem of overgeneration

-> a smaller set of expressions that uniquely identify the objects referred to in the text

application of anaphora resolution e.g. to which noun a pronoun ’he’ refers

to?

Resolving coreferences

Procedure moving through the text sentence by

sentence and analysing the nominal expressions in each sentence from left to right

either an expression is identified as a new participant in the discourse, or it is taken to refer to a previously mentioned referent

Resolving coreferences

Coreference is determined by a 3 step procedure a set of candidates is collected: all nominals

within a local segment of discourse some candidates are eliminated due to

morphological mismatches or syntactical restrictions

remaining candidates are ranked according to their relative salience in the discourse

Salience factors

sent(term) = 100 iff term is in the current sentence

cntx(term) = 50 iff term is in the current discourse segment

subj(term) = 80 iff term is a subjectacc(term) = 50 iff term is a direct objectdat(term) = 40 iff term is an indirect obj...

Local salience of a candidate

The local salience of a candidate is the sum of the values of the salience factors

the most salient candidate is selected as the antecedent for the anaphor

if the coreference link cannot be established to some other expression, the nominal is taken to introduce a new referent

-> coreferent classes

Topic stamps

In order to further reduce the referent set, some additional structure has to be imposed the term set is ranked according to the

salience of its members relative prominence or importance in the

discourse of the entities to which they refer objects in the centre of discussion have a

high degree of salience

Saliency

Measured like local saliency in coreference resolution, but tries the measure the importance of unique referents in the discourse

Priest is charged with Pope attack

A Spanish priest was charged here today with attempting to murder the Pope. Juan Fernandez Krohn, aged 32, was arrested after a man armed with a bayonet approached the Pope while he was saying prayers at Fatima on Wednesday night.

According to the police, Fernandez told the investigators today that he trained for the past six months for the assault. He was alleged to have claimed the Pope ’looked furious’ on hearing the priest’s criticism of his handling of the church’s affairs. If found quilty, the Spaniard faces a prison sentence of 15-20 years.

Saliency’priest’ is the primary element

eight references to the same actor in the body of the story

these reference occur in important syntactic positions: 5 are subjects of main clauses, 2 are subjects of embedded clauses, 1 is a possessive

’Pope attack’ is also important ’Pope’ occurs 5 times, but not in so important

positions (2 are direct objects)

Discourse segments

If the intention is to use very concise descriptions of one or two salient phrases, i.e. topic stamps, longer text have to be broken down into smaller segments

topically coherent, contiguous segments can be found by using a lexical similarity measure assumption: distribution of words used

changes when the topic changes

BG: Summarization process

Linguistic analysisdiscourse segmentationextended phrase analysisanaphora resolutioncalculation of discourse saliencetopic stamp identificationcapsule overview

Knowledge-rich approaches

Structured information can be used as the starting point for summarization structured information: e.g. data and

knowledge bases, may have been produced by processing input text

summarizer does not have to address the linguistic complexities and variability of the input, but also the structure of the input text is not available

Knowledge-rich approaches

There is a need for measures of salience and relevance that are dependent on the knowledge source

addressing coherence, cohesion, and fluency becomes the entire responsibility of the generator

STREAK, PLANDOC

McKeown, Robin, Kukich (1995): Generating concise natural language summaries

goal: folding information from multiple facts into a single sentence using concise linguistic constructions

STREAK

Produces summaries of basketball games

first creates a draft of essential factsthen uses revision rules constrained

by the draft wording to add in additional facts as the text allows

STREAK

Input: a set of box scores for a basketball game historical information (from a database)

task: summarize the highlights of the game,

underscoring their significance in the light of previous games

output: a short summary: a few sentences

STREAK

The box score input is represented as a conceptual network that expresses relations between what were the columns and rows of the table

essential facts: the game result, its location, date and at least one final game statistic (the most remarkable statistic of a winning team player)

STREAK

Essential facts can be obtained directly from the box-score

in addition, other potential facts other notable game statistics of individual

players - from box-score game result streaks (Utah recorded its

fourth straight win) - historical extremum performances such as

maximums or minimums - historical

STREAK

Essential facts are always includedpotential facts are included if there is

space decision on the potential facts to be

included could be based on the possibility to combine the facts to the essential information in cohesive and stylistically successful ways

STREAK

Given facts: Karl Malone scored 39 points. Karl Malone’s 39 point performance is

equal to his season higha single sentence is produced:

Karl Malone tied his season high with 39 points

PLANDOC

Produces summaries of telephone network planning activity

uses discourse planning, looking ahead in its text plan to group together facts which can be expressed concisely using conjunction and deleting repetitions

PLANDOC

The system must produce a report documenting how an engineer investigated what new technology is needed in a telephone route to meet demand through use of a sophisticated software planning system

PLANDOC

Input: a trace of user interaction with the planning

system software PLANoutput:

1-2 page report, including a paragraph summary of PLAN’s solution, a summary of refinements than an engineer made to the system solution, and a closing paragraph summarizing the engineer’s final proposition

Summary generationSummaries must convey maximal

information in a minimal amount of spacerequires the use of complex sentence

structures multiple modifiers of a noun or a verb conjunction (’and’) ellipsis (deletion of repetitions)

selection of words that convey multiple aspects of the information

processing of large document collections part 3. text summarization z”process of distilling the...

Documents

salient slide

text zmodeling text

task slide

informative slide

abstract information

text summarization zmany

text summarization zprocess

narrative structure