Difference between revisions of "Blog summarization: CIKM 2007"
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| − | This [[Category::paper]] aims at [[AddressesProblem::blog summarization]] by identifying important sentences of a blog by analyzing the comments made to it, and then extracting these sentences to present a summary of the blog. | + | This [[Category::paper]] aims at [[AddressesProblem::blog summarization]] by identifying important sentences of a blog by analyzing the comments made to it, and then extracting these sentences to present a summary of the blog. Authors use a Reader-Quotation-Topic model to give representativeness score to different words in user comments. Then, “significant” sentences from the blog are selected based on two methods: Density-based Selection and Summation-based selection. Authors employed humans to create summaries of the blogs to evaluate their method against. Please see the dataset page for information about dataset. Below, is presented a closer look at Reader-Quotation-Topic model and the two sentence-selection methodologies along with the results of the experiments. |
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| + | ==Reader-Quotation-Topic (ReQuT) model== | ||
| + | Each word is given a Reader, a Quotation and a Topic measure. The motivation is that words written by “authoritative” readers, or the ones found in comments which are quoted in other comments, or those that relate to mostly discussed topics, are important than others. So ReQuT scores are given to each word, and the overall importance of that word is judged by a weighted sum of the ReQuT scores. | ||
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| + | ==The Math behind this== | ||
| + | ====Reader Measure==== | ||
| + | Given the full set of comments to a blog, the authors construct a directed reader graph <math>G_R :=(V_R, E_R)</math>. Each node r a ∈ VR is a reader, and an edge eR(rb, ra) ∈ ER exists if rb mentions ra in one of rb’s comments. The weight on an edge, WR(rb, ra), is the ratio between the number of times rb mentions ra against all times rb mentions other readers (including ra). The authors compute reader authority using a ranking algorithm, shown in Equation 1, where |R| denotes the total number of readers of the blog, and d is the damping factor. | ||
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| + | A(ra) = d•1/|R| + (1-d) ∑WR(rb, ra) • A(rb)...............................................(1) | ||
| + | RM(wk) = ∑ tf(wk, ci) • A(ra)....................................................................(2) | ||
| + | The reader measure of a word wk, denoted by RM(wk), is given in Equation 2, where tf(wk, ci) is the term frequency of word wk in comment ci | ||
Revision as of 20:48, 30 March 2011
Contents
Citation
Meishan Hu, Aixin Sun and Ee-Peng Lim, "Comments-oriented blog summarization by sentence extraction ", Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 2007
Online version
Summary
This paper aims at blog summarization by identifying important sentences of a blog by analyzing the comments made to it, and then extracting these sentences to present a summary of the blog. Authors use a Reader-Quotation-Topic model to give representativeness score to different words in user comments. Then, “significant” sentences from the blog are selected based on two methods: Density-based Selection and Summation-based selection. Authors employed humans to create summaries of the blogs to evaluate their method against. Please see the dataset page for information about dataset. Below, is presented a closer look at Reader-Quotation-Topic model and the two sentence-selection methodologies along with the results of the experiments.
Reader-Quotation-Topic (ReQuT) model
Each word is given a Reader, a Quotation and a Topic measure. The motivation is that words written by “authoritative” readers, or the ones found in comments which are quoted in other comments, or those that relate to mostly discussed topics, are important than others. So ReQuT scores are given to each word, and the overall importance of that word is judged by a weighted sum of the ReQuT scores.
The Math behind this
Reader Measure
Given the full set of comments to a blog, the authors construct a directed reader graph . Each node r a ∈ VR is a reader, and an edge eR(rb, ra) ∈ ER exists if rb mentions ra in one of rb’s comments. The weight on an edge, WR(rb, ra), is the ratio between the number of times rb mentions ra against all times rb mentions other readers (including ra). The authors compute reader authority using a ranking algorithm, shown in Equation 1, where |R| denotes the total number of readers of the blog, and d is the damping factor.
A(ra) = d•1/|R| + (1-d) ∑WR(rb, ra) • A(rb)...............................................(1) RM(wk) = ∑ tf(wk, ci) • A(ra)....................................................................(2) The reader measure of a word wk, denoted by RM(wk), is given in Equation 2, where tf(wk, ci) is the term frequency of word wk in comment ci
