Manuel J. Maña-López
José M. Gómez-Hidalgo
Abstract
A more and more generalized problem in effective information access is the presence in the same corpus of multiple documents that contain similar information. Generally, users may be interested in locating, for a topic addressed by a group of similar documents, one or several particular aspects. This kind of task, called instance or aspectual retrieval, has been explored in several TREC Interactive Tracks. In this article, we propose in addition to the classification capacity of clustering techniques, the possibility of offering a indicative extract about the contents of several sources by means of multidocument summarization techniques. Two kinds of summaries are provided. The first one covers the similarities of each cluster of documents retrieved. The second one shows the particularities of each document with respect to the common topic in the cluster. The document multitopic structure has been used in order to determine similarities and differences of topics in the cluster of documents. The system is independent of document domain and genre. An evaluation of the proposed system with users proves significant improvements in effectiveness. The results of previous experiments that have compared clustering algorithms are also reported.
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Index Terms
- Multidocument summarization: An added value to clustering in interactive retrieval
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Reviews
Bei Yu
Clustering retrieved documents is a typical post-retrieval processing technique used to present an organized result set, not simply a ranked list, to the user, in order to reduce the cognitive burden of going through a large number of returned results. Some commercial search engines, such as Vivisimo, have implemented this strategy fairly successfully. However, studies show that the benefit of clustering is undermined by a poor visual connection between the clusters and the document content. By providing extra indicative extracts, covering both the similarities and particularities of each document contributing to the specific cluster, the authors of this paper take a further step toward improving retrieved documents’ organization and accessibility. With the assumption that each document consists of several subtopics, the authors first use the TextTiling algorithm to segment the documents. K-means variants are then used to cluster the text segment, and a sentence extraction-based multidocument summary is generated for each cluster, to cover common aspects using surface level information (for example, locations, headings, tf*idf values, and so on). Finally, a single summary is generated for each document indicating its originality. Commonality detection is relatively easier than difference identification, in that, for the latter problem, it is even harder to balance originality and relevance. Text segment alignment is also necessary if multiple aspects are addressed for the same topic. Regretfully, this paper does not offer a detailed solution for difference and originality detection. The authors used both objective (instance precision/recall) and subjective (questionnaires) methods to evaluate their system’s effectiveness. The objective evaluation shows that commonality summarization helps reduce the reading load by 20 to 30 percent, and it is not a surprise to see that the difference summary does not significantly help. The usability problem still remains in this approach, based on a subjective evaluation by users that it is hard to use the new system. Online Computing Reviews Service
Ian Ruthven
Simultaneously accessing large numbers of text documents is an activity that is not well supported by current search engine interfaces. Many solutions have been explored that employ some form of clustering, or document summarization, to facilitate the assessment of retrieved documents. The research outlined in this paper combines the two solutions to provide summaries of document clusters. The idea itself is not new; what is new is that some attempt has been made to exploit the topical structure of documents in the summarization process. However, this novel aspect is not well described, and other researchers may have difficulty replicating this work based on the description given. Two types of summaries are described: multi-document summaries, which emphasize the similarities between documents within a cluster, and single document summaries, which emphasize the difference between a document and other documents in the same cluster. A user evaluation, based on a standard information retrieval experimental protocol, is presented to assess the effectiveness of the clustering and summarization techniques. The evaluation compares three interfaces: a standard interface that offers lists of retrieved documents; a clustering interface offering a series of document clusters; and a summarization interface that offers clusters, a summary of each cluster, and a summary of each document. In the published version of the paper, the screen dumps are of such poor quality that they are illegible, so it is not possible to evaluate the quality or readability of the summaries created. The results are not conclusive, displaying no real user preference for one interface over the others. One of the main findings is that familiarity with the search topic correlates with a preference for the standard list interface, whereas user unfamiliarity with the topic correlates with a preference for the more advanced interfaces. It is a pity that more correlations like this, or qualitative results, were not reported, as they may form a useful guide to why users prefer one interface over another. Online Computing Reviews Service
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