Speech Summarization

Speech Summarization

Sameer R. Maskey

Summarization

‘the process of distilling the most important information from a source (or sources) to produce an abridged version for a particular user (or users) and task (or tasks) [Mani and Maybury, 1999]

Indicative or Informative

Indicative Suggests contents of the document Better suits for searchers

Informative Meant to represent the document Better suits users who want the overview

Speech Summarization

Speech summarization entails ‘summarizing’ speech Identify important information relevant to users and the

story Represent the important information Present the extracted/inferred information as an addition

or substitute to the story

Are Speech and Text Summarization similar?

Yes Identifying important

information Some lexical, discourse

features Extraction

NO! Speech Signal Prosodic features NLP tools? Segments – sentences? Generation? ASR transcripts Data size

Text vs. Speech Summarization (NEWS) Speech Signal

Speech Channels- phone, remote satellite, station

Transcripts- ASR, Close Captioned

Many Speakers- speaking styles

Prosodic Features-pitch, energy, duration

Structure-Anchor, Reporter Interaction

Commercials, Weather Report

Transcript- Manual

Some Lexical Features

Story presentationstyle

Error-free Text

Lexical Features

Segmentation-sentences

NLP tools

Speech Summarization (NEWS)Speech Signal







Transcript- Manual



Error-free Text

Lexical Features


many NLP tools

Why speech summarization?

Multimedia production and size are increasing: need less time-consuming ways to archive, extract, use and browse speech data - speech summarization, a possible solution

Due to temporal nature of speech, difficult to scan like text

User-specific summaries of broadcast news is useful Summarizing voicemails can help us better organize

voicemails

Extraction Training w/ manual Summaries

Sentence ExtractionSimilarity Measures

Concept LevelExtract concepts units

Generate Words/Phrases

Use of Structured Data

SOME SUMMARIZATIONTECHNIQUES BASED

ON TEXT (LEXICAL FEATURES)

[Salton, et al., 1995]

[McKeown, et al., 2001]

[Hovy & Lin, 1999]

[Witbrock & Mittal, 1999]

[Maybury, 1995]

Summarization by sentence extraction with similarity measures [Salton, et al., 1995]

Many present day techniques involve sentence extraction

Extract sentence by finding similar sentence to topic sentence or dissimilar sentences to already built summary (Maximal Marginal Relativity)

Find sentences similar to the topic sentence Various similarity measures [Salton, et al., 1995]

Cosine Measure Vocabulary Overlap Topic words overlap Content Signatures Overlap

“Automatic text structuring and summarization” [Salton, et al., 1995]

Uses hypertext link generation to summarize documents Builds intra-document hypertext links Coherent topic distinguished by separate chunk of links Remove the links that are not in close proximity Traverse along the nodes to select a path that defines a

summary Traverse order can be

Bushy Path: constructed out n most bushy nodes Depth first Path: Traverse the most bushy path after each node Segmented bushy path: construct bushy paths individually and

connect them on text level

Text relationship map [Salton, et al., 1995]

Build manual summaries using available number of annotators Extract set of features from the manual summaries Train the statistical model with the given set of values for manual

summaries Use the trained model to score each sentence in the test data Extract ‘n’ highest scoring sentences Various statistical models/machine learning

Regression Models Various classifiers Bayes rules for computing probability for inclusion by counting [Kupiec, et al.,

1995]

Where S is summary given k features Fj and P(Fj) & P(Fj|s of S) can be computed by counting occurrences

k

jj

k

jj

k

FP

SsPSsFP

FFFSsP

1

1,21

)(

)()|(

)...,|(

Summarization by feature based statistical models [Kupiec, et al., 1995]

Summarization by concept/content level extraction

and generation [Hovy & Lin, 1999] , [Witbrock & Mittal, 1999]

Quite a few text summarizers based on extracting concept/content and presenting them as summary

Concept Words/Themes] Content Units [Hovy & Lin, 1999] Topic Identification

[Hovy & Lin, 1999] uses Concept Wavefront to build concept taxonomy

Builds concept signatures by finding relevant words in 30000 WSJ documents each categorized into different topics

Phrase concatenation of relevant concepts/content Sentence planning for generation

Summarization of Structured text database [Maybury,

1995]

Summarization of text represented in a structured form: database, templates Report generation of a medical history from a database is

such an example

Link analysis (semantic relations within the structure)

Domain dependent importance of events

events all of # Total

event E of soccurrence of # Eoffrequency Relative

Speech summarization: present

Speech Summarization seems to be mostly based on extractive summarization

Extraction of words, sentences, content units Some compression methods have also been proposed Generation as in some text-summarization techniques is

not available/feasible Mainly due to the nature of the content

Word scoringwith dependency structure

Sentence extraction withsimilarity measures

Classification

User access information

Weighted finite statetransducers

Removing disfluencies

SPEECH SUMMARIZATIONTECHNIQUES

[Christensen et al., 2004]

[Hori C. et al., 1999, 2002] , [Hori T. et al., 2003]

[Koumpis & Renals, 2004]

[He et al., 1999]

[Hori T. et al., 2003]

[Zechner, 2001]

Content/Context sentence level extraction for speech

summary [Christensen et al., 2004]

These are commonly used speech summarization

techniques: finding sentences similar to the lead topic sentences

Using position features to find the relevant nearby sentences after detecting the topic sentence

where Sim is a similarity measure between two sentences

)},({maxarg 1/

^

iEDs

k ssSimsSi

)},({maxarg/

^

iEDs

k sDSimsSi

Weighted finite state transducers for speech

summarization [Hori T. et al., 2003]

Speech Summarization includes speech recognition, paraphrasing, sentence compaction integrated into single Weighted Finite State Transducer

Enables decoder to employ all the knowledge sources in one-pass strategy Speech recognition using WFST

Where H is state network of triphone HMMs, C is triphone connection rules, L is pronunciation and G is trigram language model

Paraphrasing can be looked at as a kind of machine translation with translation probability P(W|T) where W is source language and T is the target language

If S is the WFST representing translation rules and D is the language model of the target language speech summarization can bee looked at as the following composition

GLCHR

DSGLCHZ

H C L G S DSpeech recognizer Translator

Speech Translator

User access information for finding salient parts [He et al., 1999]

Idea is to summarize lectures or shows extracting the parts that have been viewed the longest

Needs multiple users of the same show, meeting or lecture for a statistically significant training data

For summarizing lectures compute the time spent on each slide

Summarizer based on user access logs did as well as summarizers that used linguistic and acoustic features

Average score of 4.5 on a scale of 1 to 8 for the summarizer (subjective evaluation)

Word level extraction by scoring/classifying words [Hori C. et al., 1999, 2002]

Score each word in the sentence and extract a set of words to form a sentence whose total score is the product/sum of the scores of each word

Example: Word Significance score (topic words) Linguistic Score (bigram probability) Confidence Score (from ASR) Word Concatenation Score (dependency structure

grammar)

Where M is the number of words to be extracted, and I C T are weighting factors for balancing among L, I, C, and T r

)()()()...|({)( ,11

1 mmTmcm

M

mImm vvTrvCvIvvLVS

Assumptions

There are a few assumptions made in the previously mentioned methods Segmentation Information Extraction Automatic Speech Recognition Manual Transcripts Annotation

Speech Segmentation?

segmentation

speech

textExtraction

•Features•Techniques•Evaluation

•Sentences•Topics

•Text Retrieval Methods on ASR Transcripts

Segmentation Sentences Stories Topic Speaker

Information Extraction from Speech Data?

segmentation

speech

textExtraction

•Features•Techniques•Evaluation

•Sentences•Topics

•Text Retrieval Methods on ASR Transcripts

Information Extraction Named Entities Relevant Sentences and Topics Weather/Sports Information

Audio segmentation

Audio Segmentation

Topics Story

Commercials

Sentences

Weather

Speaker GenderSpeakerTypes

Audio segmentation methods

Can be roughly categorized in two different categories Language Models [Dharanipragada, et al., 1999] , [Gotoh &

Renals, 2000], [Maybury, 1998], [Shriberg, et al., 2000] Prosody Models [Gotoh & Renals, 2000], [Meinedo & Neto,

2003] , [Shriberg, et al., 2000] Different methods work better for different purposes and

different styles of data [Shriberg, et al., 2000] Discourse cues based method highly effective in broadcast

news segmentation [Maybury, 1998] Prosodic model outperforms most of the pure language

modeling methods [Shriberg, et al., 2000], [Gotoh & Renals, 2000]

Combined model of using NLP techniques on ASR transcripts and prosodic features seem to work the best

Overview of a few algorithms:statistical model [Gotoh & Renals, 2000]

Sentence Boundary Detection: Finite State Model that extracts boundary information from text and audio sources

Uses Language and Pause Duration Model Language Model: Represent boundary as two classes with “last word”

or “not last word” Pause Duration Model:

Prosodic features strongly affected by word Two models can be combined Prosody Model outperforms language model Combined model outperforms both

Segmentation using discourse cues [Maybury,

1998]

Discourse Cues Based Story Segmentation Sentence segmentation is not possible with this method Discourse Cues in CNN

Start of Broadcast Anchor to Reporter Handoff, Reporter to Anchor Handoff Cataphoric Segment (still ahead of this news) Broadcast End

Time Enhanced Finite State Machine to represent discourse states such as anchor, reporter, advertisement, etc

Other features used are named entities, part of speech, discourse shifts “>>” speaker change, “>>>” subject change

Source Precision Recall

ABC 90 94

CNN 95 75

Jim Lehrer Show 77 52

Speech Segmentation

Segmentation methods essential for any kind of extractive speech summarization

Sentence Segmentation in speech data is hard Prosody Model usually works better than Language

Model Different prosody features useful for different kinds of

speech data Pause features essential in broadcast news segmentation Phone duration essential in telephone speech

segmentation Combined linguistic and prosody model works the best

Information Extraction from Speech

Different types of information need to be extracted depending on the type of speech data

Broadcast News: Stories [Merlino, et al., 1997] Named Entities [Miller, et al., 1999] , [Gotoh & Renals,

2000] Weather information

Meetings Main points by a particular speaker Address Dates

Voicemail Phone Numbers [Whittaker, et al., 2002] Caller Names [Whittaker, et al., 2002]

Statistical model for extracting named entities [Miller, et al., 1999] , [Gotoh & Renals, 2000]

Statistical Framework: V denote vocabulary and C set of name classes,

Modeling class information as word attribute: Denote e=<c, w> and model using

In the above equation ‘e’ for two words with two different classes are considered different. This bring data sparsity problem

Maximum likelihood estimates by frequency counts Most probable sequence of class names by Viterbi algorithm

Precision and recall of 89% for manual transcript with explicit modeling

mi

iim eeepeep..1

111 ),...|(),...,(

),,...,,(... 11 maxarg..1

m

cc

m wcwcpccm

Named entity extraction results [Miller, et al., 1999]

BBN Named Entity Performance as a function of WER [Miller, et al., 1999]

Information Extraction from Speech

Information Extraction from speech data essential tool for speech summarization

Named Entities, phone number, speaker types are some frequently extracted entities

Named Entity tagging in speech is harder than in text because ASR transcript lacks punctuation, sentence boundaries, capitalization, etc

Statistical models perform reasonably well on named entity tagging

Speech Summarization at Columbia We make a few assumptions in segmentation and

extraction Some new techniques proposed 2-level summary

Headlines for each story Summary for each story

Summarization Client and Server model

Speech Summarization (NEWS)Speech Signal







Transcript- Manual



Error-free Text

Lexical Features


many NLP tools

ACOUSTIC

LEXICAL

DISCOURSE

STRUCTURAL

Speech SummarizationINPUT:

TranscriptsTranscripts+

ACOUSTIC LEXICAL DISCOURSE STRUCTURAL

/Sentence Segmentation, Speaker Identification, Speaker Clustering, Manual Annotation,

2-Level Summary Headlines

Summary

StoryNamed Entity Detection, POS tagging

Corpus

Topic Detection and Tracking Corpus (TDT-2) We are using 20 “CNN Headline shows” for

summarization 216 stories in total 10 hours of speech data Using Manual transcripts, Dragon and BBN ASR

transcripts

Annotations - Entities

We want to detect – Headlines Greetings Signoff SoundByte SoundByte-Speaker Interviews

We annotated all of the above entities and the named entities (person, place, organization)

Annotations – by Whom and How?

We created a labeling manual following ACE standards

Annotated by 2 annotators over a course of a year 48 hours of CNN headlines news in total We built a labeling interface dLabel v2.5 that

went through 3 revisions for this purpose

Annotations - dLabel v2.5

Annotations – ‘Building Summaries’ 20 CNN shows annotated for extractive summary A Brief Labeling Manual No detailed instruction on what to choose and

what not to? We built a web-interface for this purpose, where

annotator can click on sentences to be included in the summary

Summaries stored in a MySQL database

Annotations – Web Interface

Annotations – Web Interface

Acoustic Features F0 features

max, min, mean, median, slope Change in pitch may be a topic shift

RMS energy feature max, min, mean

Higher amplitude probably means a stress on the phrases Duration

Length of sentence in seconds (endtime – starttime) Very short or a long sentence might not be important for summary

Speaker Rate how fast the speaker is speaking

Slower rate may mean more emphasis in a particular sentence

Acoustic Features – Problems in Extraction What should be the segment to extract these features –

sentences, turn, stories?

We do not have sentence boundaries.

A dynamic programming aligner to align manual sentence boundary with ASR transcripts

Feature values needs to be normalized by speaker: used Speaker Cluster ID available from BBN ASR

Acoustic Features – Praat: Extraction Tool

Lexical Features

Named Entities in a sentence Person People Organization Total count of named entities

Num. of words in a sentence Num. of words in previous and next sentence

Lexical Features - Issues

Using Manual Transcript Sentence boundary detection using Ratnaparkhi’s

mxterminator Named Entities annotated For ASR transcript:

Sentence boundaries aligned Automatic Named Entities detected using BBN’s Identifinder Many NLP tools fail when used with ASR transcript

Structural Features

Position Position of the sentence in the story and the turn Turn position in the show

Speaker Type Reporter or Not

Previous and Next Speaker Type Change in Speaker Type

Discourse Feature Given-New Feature Value Computed using the following equation

dt

s

d

niS ii

)(

Intuition: ‘newness’ ~ more new unique nouns in the sentence (ni/d) If many nouns already seen in the sentence ~ higher ‘givenness’

s_i/(t-d)

where n_i is the number of ‘new’ noun stems in sentence i, d is the total

number of unique nouns, s_i is the number of noun stems that have already

been seen, t is the total number of nouns

Experiments Sentence Extraction as a summary Binary Classification problem

‘0’ not in the summary ‘1’ in the summary

10 hours of CNN news shows 4 different sets of features – acoustic, lexical, structural, discourse 10 fold-cross validation 90/10 train and test 4 different classifiers WEKA and YALE learning tool Feature Selection Evaluation using F-Measure and ROUGE metrics

Feature Sets

We want to compare the various combination of our “4” feature sets Acoustic/Prosodic (A) Lexical (L) Structural (S) Discourse (D)

Combinations of feature sets, 15 in total L, A, …, L+A, L+S, … , L+A+S, … , L+S+D, … , L+A+S+D

Classifiers

Choice of available classifier may affect the comparison of feature sets

Compared 4 different classifiers by plotting threshold (ROC) curve and computing Area Under Curve (AOC)

Best Classifier has AOC of 1

Classifier AOC

Bayesian Network 0.771

C4.5 Decision Trees 0.647

Ripper 0.643

Support Vector Machines

0.535

ROC CurvesROC curves for some classifiers

0

100

200

300

400

500

600

700

800

-500 0 500 1000 1500 2000 2500 3000

False Positives (Specificity)

Tru

e P

osi

tive

s (S

ensi

tivi

ty)

bayesNet

decisiontable

decisiontreeJ48

Results – Best Combined Feature Set

We obtained best F-measure for 10 fold cross validation using all acoustic (A), lexical (L), discourse (D) and structural (S) feature.

Precision Recall F-Measure

Baseline 0.430 0.429 0.429

L+S+A+D 0.489 0.613 0.544

F-Measure is 11.5% higher than the baseline.

What is the Baseline?

Baseline is the first 23% of sentences in each story. In Average Model summaries were 23% in length

In summarization selecting first n% of sentences is pretty standard baseline For our purpose this is a very strict baseline, why?

Because stories are short. In average 18.2 sentences for each story

In broadcast news it is standard to summarize the story in the introduction

These sentences are likely to be in the summary

Baseline and the Best F-measure

0.400

0.450

0.500

0.550

0.600

0.650

0.700

Precision Recall F-Measure

Metric

Sc

ore

(-bs-)

LS

LSD

LA

LAD

LSA

LADS

0.679

0.5500.531

0.4300.443 0.447 0.443

0.481

0.455

0.483 0.482 0.4830.463 0.467

0.486 0.489

0.074

0.235

0.302

0.429

0.4950.512

0.542

0.505

0.542

0.511

0.543 0.547

0.6040.618

0.608 0.613

0.133

0.329

0.385

0.429

0.4680.478

0.487 0.493 0.495 0.4970.511 0.513

0.524 0.532 0.540 0.544

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

D S SD (-bs-) A AD L SA LD SAD LS LSD LA LAD LSA LADS

Feature Sets

Sc

ore

Precision

Recall

F-Measure

F-Measure for All 15 Feature Sets

Evaluation using ROUGE

F-measure is a too strict measure Predicted summary sentences has to match

exactly with the summary sentences What if we have a predicted sentence that is not

an exact but has a similar content? ROUGE takes account of this

ROUGE metric

Recall-Oriented Understudy for Gisting Evaluation (ROUGE) ROUGE-N (where N=1,2,3,4 grams) ROUGE-L (longest common subsequence) ROUGE-S (skip bigram) ROUGE-SU (skip bigram counting unigrams as well)

Evaluation using ROUGE metricROUGE-1

ROUGE-2

ROUGE-3

ROUGE-4

ROUGE-L

ROUGE-S

ROUGE-SU

Baseline 0.58 0.51 0.50 0.49 0.57 0.40 0.41

L+S+A+D 0.84 0.81 0.80 0.79 0.84 0.76 0.76

In average L+S+A+D is 30.3% higher than the baseline

Results - ROUGE

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

D S SD (-bs-) AD A LD L SA LSD LS SAD LAD LA LSA LADS

Feature Se ts

Sco

re

ROUGE-S*

ROUGE-SU*

ROUGE-4

ROUGE-3

ROUGE-2

ROUGE-L

ROUGE-1

Does importance of ‘what’ is said correlates with ‘how’ it is said? Hypothesis: “Speakers change their amplitude, pitch,

speaking rate to signify importance of words, phrases, sentences.”

If this is the case then the prediction labels for sentences predicted using acoustic features (A) should correlate with labels predicted using lexical features (L)

We found correlation of 0.74

This above correlation is a strong support for our hypothesis

Is It Possible to Build ‘good’ Automatic Speech Summarization Without Any Transcripts?

Feature Set F-Measure ROUGE-avg

L+S+A+D 0.54 0.80

L 0.49 0.70

S+A 0.49 0.68

A 0.47 0.63

Baseline 0.43 0.50

Just using A+S without any lexical features we get 6% higher F-measure and 18% higher ROUGE-avg than the baseline

Feature selection We used feature selection to find the best feature set among all the features in

the combined set 5 best features are shown in the table These 5 features consist of all 4 different feature sets Feature Selection also selected these 5 features as the optimal feature set F-measure using just 5 features is 0.53 which only 1% lower than using all

features

Rank Type Feature

1 A Time Length in seconds

2 L Num. of words

3 L Tot. Named Entities

4 S Normalized Sent. Pos

5 D Given-New Score

Problems and Future Work

We assume we have a good Sentence boundary detection Speaker IDs Named Entities

We obtain a very good speaker IDs and named entities from BBN but no sentence boundaries

We have to address the sentence boundary detection as a problem on its own.

Alternative solution: We can do a ‘breath group’ level segmentation and build a model based on such segmentation

More Current and Future Work

We annotated headlines, greetings, signoffs, interviews, soundbytes, soundbyte speakers, interviewees

We want to detect these entities (students involved for detecting some of these entities – Aaron

Roth, Irina Likhtina)

We want to present summary and these entities in a unified browsable frame work (student involved – Lauren Wilcox)

The browser is implemented in client/server framework

SPEECH

ASR

MANUAL

INPUT

SGML Parser

XML Parser

TranscriptAligner

SentenceParser

TextPreProcess

or

PRE-PROCESSINGFEATURE

EXTRACTION

ACOUSTIC

LEXICAL

STRUCTURE

DISCOURCE

SEGMENTATION/DETECTION

SEGMENTATION

Story

Headline

Interviews

Q&A

DETECTION

SoundBites

SoundBite-Speaker

Signon/off

Weather forecast

Named-Entity Tagger

SUMMARIZER

MINING

MODEL/PRESENTATION

SPEECH

MAN-UAL

ASR

CHUNKS

CC

Summarization Architecture

SENT27 a trial that pits the cattle industry against tv talk show host oprah winfrey is under way in amarillo , texas.

SENT28 jury selection began in the defamation lawsuit began this morning . SENT29 winfrey and a vegetarian activist are being sued over an exchange on her April 16, 1996 show . SENT30 texas cattle producers claim the activists suggested americans could get mad cow disease from

eating beef . SENT31 and winfrey quipped , this has stopped me cold from eating another burger SENT32 the plaintiffs say that hurt beef prices and they sued under a law banning false and disparaging

statements about agricultural products SENT33 what oprah has done is extremely smart and there's nothing wrong with it she has moved her

show to amarillo texas , for a while SENT34 people are lined up , trying to get tickets to her show so i'm not sure this hurts oprah . SENT35 incidentally oprah tried to move it out of amarillo . she's failed and now she has brought her

show to amarillo . SENT36 the key is , can the jurors be fair SENT37 when they're questioned by both sides, by the judge , they will be asked, can you be fair to both

sides SENT38 if they say , there's your jury panel SENT39 oprah winfrey's lawyers had tried to move the case from amarillo , saying they couldn't get an

impartial jury SENT40 however, the judge moved against them in that matter …

story summary

Generation or Extraction?

Conclusion

We talked about different techniques to build summarization systems

We described some speech-specific summarization algorithms

We showed feature comparison techniques for speech summarization A model using a combination of lexical, acoustic, discourse and

structural feature is one of the best model so far. Acoustic features correlate with the content of the sentences

We discussed possibilities of summarizing speech without any transcribed text

Documents

Speech Summarization