DOCUMENT UPDATE SUMMARIZATION USING INCREMENTAL HIERARCHICAL CLUSTERING CIKM’10 (DINGDING WANG, TAO LI) Advisor: Koh, Jia-Ling Presenter: Nonhlanhla Shongwe 1

Preview Introduction Incremental Hierarchical Clustering Based Document Update Summarization Incremental Hierarchical Sentence Clustering (IHSC) o The COBWEB algorithm o COBWEB for text Algorithm Evaluation measures Experiments and results 2

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 3

Introduction cont’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 4

Introduction cont’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 5

Introduction cont’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 6

Introduction cont’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. 7

INCREMENTAL HIERARCHICAL CLUSTERING BASED DOCUMENT UPDATE SUMMARIZATION 1. Framework 2. Preprocessing 3. Incremental Hierarchical Sentence Clustering (IHSC) I.The COBWEB algorithm II.COBWEB for text 4. Representative Sentence Selection for Each Node of the Hierarchy 5. The Algorithm 8

Framework 9

Preprocessing Data preprocessing Given a collection of documents 1.Decompose the documents into sentences 2.Stop words are removed 3.Word stemming is performed 4.Sentence matrix is constructed and each element is the term frequency 10

Incremental Hierarchical 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 11

The 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 12

The COBWEB algorithm cont’s Ai = The ith attribute of the items being clustered Vij = jth value of the ith attribute For example: A1 Є {male, female}, A2 Є {Red, Green, Blue} V12= female V22= Green Probability matching guessing strategy Expected number of times we can correctly guess the value of multinomial variable Ai to be Vij for an item in a cluster k A 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 13

The COBWEB algorithm cont’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 14

The COBWEB algorithm cont’s Example: 15

COBWEB 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 16

COBWEB for text cont’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) / cf cf = collection frequency = Pr(the word repeats | the word occurs ) Therefore: (1 - p) = the probability of the word occurring only once 17

COBWEB for text cont’s 18 Substitute with p K=0, using p δk =1 Adding both formulas p(0) = 1- αp α = (1-p(0))/p

COBWEB for text cont’s Where attribute value f=Vij to the contribution of the attribute i towards the category utility of the cluster k 19

Representation sentence selection for Each Node of the 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 20

Representation sentence selection for Each Node of the Hierarchy cont’s Case 1 : Insert a sentence into cluster k Recalculate the representative sentence R k of cluster K Where K : number of sentences in the cluster Sim() : similarity function between sentence pairs Cosine similarity α = parameter α =

Representation sentence selection for Each Node of the Hierarchy cont’s Case 2: Create a new cluster k Newly sentence represents a new cluster R k = s new Case 3: Merge two clusters (cluster a and cluster b ) into a new cluster ( cluster c ) Sentence obtaining the higher similarity with the query is selected as the representative sentence at the new merged node 22

Representation sentence selection for Each Node of the Hierarchy cont’s Case 4: split cluster into a set of clusters (cluster a into cluster 1, cluster 2,…cluster n ) 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 23

The 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) 24

The Algorithm cont’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 25

The Algorithm cont’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 summarization 5.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 26

The Algorithm cont’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 27

EXPERIMENTS Data Description Baselines Evaluations Measures Experimental Results 28

Data 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 29

Baselines BaselineDescription RandomSelects sentences randomly for each document collection CentroidExtracts sentences according to centroid value, positional value and first sentence overlap LexPageRankConstructs a sentence connectivity graph based on cosine similarity then selects important sentences based on the concepts of eigenvector centrality LSAPerforms latent semantic analysis on terms by sentences matrix to select sentences having the greatest combined weights across all important topics 30 Implemented the following used multi-document summarization methods as the baseline systems

Evaluations Measures Rouge toolkit To compare with the human summaries MethodDescription ROUGE-1Uses unigrams ROUGE-2Uses bigrams ROUGE-LUses the longest common subsequence (LCS) ROUGE-SUSkip-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 32

Experimental Results cont’s 33

Experimental Results cont’s 34

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. 35

THANK YOU! 36