Identifying commented passages of documents using implicit hyperlinks

Abstract

This paper addresses the issue of automatically selecting passages of blog posts using readers' comments. The problem is difficult because: (i) the textual content of blogs is often noisy, (ii) comments do not always target passages of the posts and, (iii) comments are not equally useful for identifying important passages. We have developed a system for selecting commented passages which takes as input blog posts and their comments and delivers, for each post, the sentences of the post which are the most commented and/or the most discussed. Our approach combines three steps to identify commented passages of a post. The first step is to remove the complexity of processing the contents of posts and comments using heuristics adapted to the language of the blog. The second step is to find useful comments and assigns them a degree of relevance using a model automatically built and validated by an expert. The third step is to identify important passages using relevant comments. We conducted two experiments to evaluate the usefulness and the effectiveness of our approach. The first study show that in only 50% of the posts, the most commented sentence elicited by our approach corresponds to the post extract generated using generic summarization. In the second study, human participants confirmed that, in practice, selected passages are frequently commented passages.

Comments-oriented blog summarization by sentence extraction

“…Comments associated with blog posts were however not used. The problem of comments-oriented blog summarization is quite related to the problem of identifying most commented sentences reported in [3]. Comments are represented and clustered using feature vectors, and a human expert is involved to select the clusters of interest.…”

“…The weight on an edge, WQ(cj , ci), is 1 over the number of comments that cj ever quoted. We derive the quotation degree D(ci) of a comment ci using Equation 3. A comment that is not quoted by any other comment receives a quotation degree of 1/|C| where |C| is the number of comments associated with the given post.…”

Measuring the descriptiveness of web comments

“…[10]), or to evaluate sentiment analysis models [9]. Some use comments to extract sentences from blog posts for summarization purposes [2,5]. Note that the relation of comments to the commented object is not analyzed in particular.…”

Semantic tagging and classification of blogs

“…Blog comments are regarded by most bloggers as vital to the interactive nature of blogs [5]. Taking into account existing blog comments could help produce more reader-oriented summaries [6]. Despite this, quantitative studies of blogs focus on post data, leaving out the comments.…”