This paper presents a novel research problem on joint discovery of commonalities and differences between two individual documents (or document sets), called Comparative Document Analysis (CDA). Given any pair of documents from a document collection, CDA aims to automatically identify sets of quality phrases to summarize the commonalities of both documents and highlight the distinctions of each with respect to the other informatively and concisely. Our solution uses a general graph-based framework to derive novel measures on phrase semantic commonality and pairwise distinction , and guides the selection of sets of phrases by solving two joint optimization problems. We develop an iterative algorithm to integrate the maximization of phrase commonality or distinction measure with the learning of phrase-document semantic relevance in a mutually enhancing way. Experiments on text corpora from two different domains---scientific publications and news---demonstrate the effectiveness and robustness of the proposed method on comparing individual documents. Our case study on comparing news articles published at different dates shows the power of the proposed method on comparing document sets.
In particular, our work is related to @cite_28 since both try to derive common and distinct terms for a pair of related documents, but their work focuses on finding topic-related terms and selecting sentences based on such terms. They assume terms appearing in both documents as the common ones, and treat the remaining terms as distinct ones. As shown in our experiments, this method (labeled as WordMatch) suffers from low recall in finding common terms and low precision in finding distinct terms since it ignores semantic common phrases and pairwise distinct phrases. Similarly, Zhang al @cite_7 consider both common and distinct aspects in generating comparative summary for patent documents. They derive a term co-occurrence tree which can be used to extract sentences for summarization. However, they use all shared noun phrases between two documents as common terms, and apply feature selection techniques to find distinct terms. This method (see PatentCom in Sec. ) demonstrates poor performance on finding common terms due to the ignorance of semantic common phrases; although this method performs well in terms of the recall for distinct phrases, it achieves low precision, since it fails to model phrase generality and produce many overly-specific phrases.
@cite_28 @cite_7
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We describe a new method for summarizing similarities and differences in a pair of related documents using a graph representation for text. Concepts denoted by words, phrases, and proper names in the document are represented positionally as nodes in the graph along with edges corresponding to semantic relations between items. Given a perspective in terms of which the pair of documents is to be summarized, the algorithm first uses a spreading activation technique to discover, in each document, nodes semantically related to the topic. The activated graphs of each document are then matched to yield a graph corresponding to similarities and differences between the pair, which is rendered in natural language. An evaluation of these techniques has been carried out. Patent document retrieval, as a recall-orientated search task, does not allow missing relevant patent documents due to the great commercial value of patents and significant costs of processing a patent application or patent infringement case. Thus, it is important to retrieve all possible relevant documents rather than only a small subset of patents from the top ranked results. However, patents are often lengthy and rich in technical terms, and it often requires enormous human efforts to compare a given document with retrieved results. In this paper, we formulate the problem of comparing patent documents as a comparative summarization problem, and explore automatic strategies that generate comparative summaries to assist patent analysts in quickly reviewing any given patent document pairs. To this end, we present a novel approach, named PatentCom, which first extracts discriminative terms from each patent document, and then connects the dots on a term co-occurrence graph. In this way, we are able to comprehensively extract the gists of the two patent documents being compared, and meanwhile highlight their relationship in terms of commonalities and differences. Extensive quantitative analysis and case studies on real world patent documents demonstrate the effectiveness of our proposed approach.