DOCUMENT UPDATE DOCUMENT UPDATE SUMMARIZATION USING SUMMARIZATION USING

INCREMENTAL HIERARCHICAL INCREMENTAL HIERARCHICAL CLUSTERING CLUSTERING CIKM’10 (DINGDING WANG, TAO LI)CIKM’10 (DINGDING WANG, TAO LI)

Advisor: Koh, Jia-Ling Presenter: Nonhlanhla Shongwe

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Preview Preview • Introduction• Incremental Hierarchical Clustering

Based Document Update Summarization• Incremental Hierarchical Sentence

Clustering (IHSC)oThe COBWEB algorithmoCOBWEB for text• Algorithm

• Evaluation measures • Experiments and results

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Introduction Introduction • Document summarization has

been receiving much attention due to• Increasing number of documents on

the internet• Helping readers to extract their

interested information efficiently • Most document summarization

techniques perform in a batch mode

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Introduction Introduction cont’scont’s

• Two most widely used summarization methods

• Firstly: Clustering based• Term sentence matrices formed from

the document• Sentences are grouped into different

clusters• Score is attached to each sentence

using average cosine similarity• Sentences with the highest score in

each cluster form the summary

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Introduction Introduction cont’scont’s

• Secondly: Graph-ranking based• Constructs a sentence graph, each node

is a sentence in a document collection• An edge is formed between sentence

pairs if• The similarity between a pair of sentence is

above the threshold• They belong to the same document

• Sentences are selected to form the summary by voting from their neighbors

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Introduction Introduction cont’scont’s

• With the rapid growth of document, • There is a necessity to update the

existing summaries when new documents arrives.

• Traditional methods are not suitable for this task• Most of the methods work in batch way:• Meaning that all the documents need to be

process again once new documents come, which causes inefficiency

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Introduction Introduction cont’scont’s

• In this paper • To integrate document

summarization techniques into an incremental hierarchical clustering framework • To be able to re-organize sentence

clusters immediately after new documents arrive so that their corresponding summaries can be updated efficiently.

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INCREMENTAL HIERARCHICAL INCREMENTAL HIERARCHICAL CLUSTERING BASED DOCUMENT CLUSTERING BASED DOCUMENT UPDATE SUMMARIZATIONUPDATE SUMMARIZATION

1. Framework 2. Preprocessing3. Incremental Hierarchical Sentence Clustering (IHSC)

I. The COBWEB algorithm

II. COBWEB for text4. Representative Sentence Selection for Each Node of the

Hierarchy5. The Algorithm

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Framework Framework

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Preprocessing Preprocessing

• Data preprocessing • Given a collection of documents

1. Decompose the documents into sentences

2. Stop words are removed 3. Word stemming is performed4. Sentence matrix is constructed and each

element is the term frequency

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Incremental Hierarchical Incremental Hierarchical Sentence Clustering (IHSC)Sentence Clustering (IHSC)• For update summarization system• Used an Incremental Hierarchical

Clustering (IHC)• Benefits of IHC method• The method can efficiently process the

dynamic documents, new documents are added• A hierarchy is built to facilitate users• The number of clusters is not pre-

defined

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The COBWEB algorithmThe COBWEB algorithm

• Used COBWEB, most popular incremental hierarchical clustering algorithms• Based on the heuristic measures called

Category Utility (CU)

• Clusters • Probability of a document belong to a cluster• Total number of clusters K

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The COBWEB algorithm The COBWEB algorithm cont’scont’s

• Ai = The ith attribute of the items being clustered

• Vij = jth value of the ith attributeFor example:A1 Є {male, female} , A2 Є {Red, Green, Blue}V12= female V22= Green

Probability matching guessing strategyExpected number of times we can correctly guess the value of multinomial variable Ai to be Vij for an item in a cluster kA good cluster, in which the attributes of the items take similar values will have high values

COBWEB maximizes sum score over all possible assignment of a document to a cluster

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The COBWEB algorithm The COBWEB algorithm cont’scont’s

• The COBWEB algorithm can perform• Insert: add the sentence into an existing

cluster • Create: create a new cluster • Merge: combine two clusters into a single

cluster• Split: divide an existing cluster into several

clusters

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The COBWEB algorithm The COBWEB algorithm cont’s cont’s

Example:

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COBWEB for textCOBWEB for text

• The COBWEB algorithm • Using normal attributes distribution is

not suitable for text data• Documents • Are represented in the “bag of words”

where terms are attributes• Best method• Calculating CU using Katz’s

distribution

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COBWEB for text COBWEB for text cont’scont’s

• Katz’s model• Assuming word i occurs k times in

document then

= 1 – (df/N)df = document frequency N = total number of

documents

p = (cf - df) / cfcf = collection frequency

= Pr(the word repeats | the word occurs )

Therefore:(1 - p) = the probability of the word occurring only once

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COBWEB for text COBWEB for text cont’scont’s

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Substitute with p

K=0, using p δk =1Adding both formulasp(0) = 1- αpα = (1-p(0))/p

COBWEB for text COBWEB for text cont’scont’s

Where attribute value f=Vij

to the contribution of the attribute i towards the category utility of the cluster k

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Representation sentence Representation sentence selection for Each Node of the selection for Each Node of the Hierarchy Hierarchy • Update summarization system• Select the most representative

sentences to summarize each node and subtrees• Once a new sentence arrives, the

sentence hierarchy is changed by either of the four operations

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Representation sentence Representation sentence selection for Each Node of the selection for Each Node of the Hierarchy Hierarchy cont’scont’s

• Case 1 : Insert a sentence into cluster k• Recalculate the representative sentence Rk of

cluster K

• Where • K : number of sentences in the cluster• Sim() : similarity function between sentence pairs• Cosine similarity

• α = parameter • α = 0.6

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Representation sentence Representation sentence selection for Each Node of the selection for Each Node of the Hierarchy Hierarchy cont’scont’s• Case 2: Create a new clusterk

• Newly sentence represents a new cluster• Rk = snew

• Case 3: Merge two clusters (clustera and clusterb ) into a new cluster (clusterc)• Sentence obtaining the higher similarity with

the query is selected as the representative sentence at the new merged node

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Representation sentence Representation sentence selection for Each Node of the selection for Each Node of the Hierarchy Hierarchy cont’scont’s• Case 4: split cluster into a set of

clusters• (clustera into cluster1, cluster2,…clustern)• Remove node a • Substitute it using the roots of its sub-

trees• Corresponding representative sentences

are the representative sentences for the original sub-tree roots

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The AlgorithmThe Algorithm• Input: a query/topic the user is interested in

a sequence of documents/sentences

1. Read one sentence and check if it is relevant to the given topic i.e., checkrelevance(sentence,topic)

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The Algorithm The Algorithm cont’scont’s

2. If relevant :initialize the hierarchy tree, sentence as the root

Otherwise: remove it and read in the next sentence and repeat Step1

: until root node is formed

3. repeat

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The Algorithm The Algorithm cont’scont’s

4. Read in the next sentence, start from the root node• If the node is a leaf, go to Step 5 otherwise

choose one of the following with the highest CU score

1. Insert a node and conduct case 1 summarization

2. Create a node and conduct case 2 summarization

3. Merge a node and conduct case 3 summarization

4. Split a node and conduct case 4 summarization5. If a leaf node is reached, create a new leaf node

and merge the old leaf and the new leaf into a node and case 2 and case 3 are conducted

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The Algorithm The Algorithm cont’scont’s

6. Until the stopping condition is satisfied

7. Cut the hierarchy tree at one layer to obtain a summary with the corresponding length.

• Output: A sentence hierarchy

The updated summary

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EXPERIMENTS EXPERIMENTS Data DescriptionBaselinesEvaluations MeasuresExperimental Results

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Data DescriptionData Description• Hurricane Wilman

Releases(Hurricane)• 1700 documents divided into 3 phases

• TAC 2008 Update Summarization Track (TAC08)• Benchmark dataset from update

summarization • 48 topics and 20 newswire articles in

each topic

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BaselinesBaselines

Baseline Description

Random Selects sentences randomly for each document collection

Centroid Extracts sentences according to centroid value, positional value and first sentence overlap

LexPageRank

Constructs a sentence connectivity graph based on cosine similarity then selects important sentences based on the concepts of eigenvector centrality

LSA Performs latent semantic analysis on terms by sentences matrix to select sentences having the greatest combined weights across all important topics

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• Implemented the following used multi-document summarization methods as the baseline systems

Evaluations MeasuresEvaluations Measures• Rouge toolkit• To compare with the human

summaries

Method Description

ROUGE-1 Uses unigrams

ROUGE-2 Uses bigrams

ROUGE-L Uses the longest common subsequence (LCS)

ROUGE-SU Skip-bigram plus unigram 31

• Count match(gram n) maximum number of n-grams co-occurring in a candidate summary• Count(gram n) number of n-grams in the reference summaries

Experimental Results Experimental Results

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Experimental Results Experimental Results cont’scont’s

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Experimental Results Experimental Results cont’scont’s

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Conclusion Conclusion • Traditional methods perform in batch

way and are not suitable of incrementing summaries

• Incremental Hierarchical Clustering Based Document Update Summarization

• Incremental Hierarchical Sentence Clustering (IHSC)• Algorithm called COBWEB for text• Can perform Insert, Create, Merge, Split

• IHSC outperforms the traditional methods and its more efficient.

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THANK YOU!THANK YOU!

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