Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization (2021)

Abstract: Manifold ranking has been successfully applied in query-oriented multi-document summariza-tion. It not only makes use of the relationships among the sentences but also the relationships between the given query and the sentences. However the information of original query is often insufficient. So we present a query expansion method which is combined in the manifold rank-ing to resolve this problem. Our method not only utilizes the information of the query term itselfand the knowledge base WordNet to expand it by synonyms but also uses the information of the document set itself to expand the query in various ways (mean expansion variance expansionand TextRank expansion). Compared with the previous query expansion methods our methodcombines multiple query expansion methods to better represent query information and at the same time it makes a useful attempt on manifold ranking. In addition we use the degree of wordoverlap and the proximity between words to calculate the similarity between sentences. We per-formed experiments on the datasets of DUC 2006 and DUC2007 and the evaluation results showthat the proposed query expansion method can significantly improve the system performance andmake our system comparable to the state-of-the-art systems.

## introduction query-focused multi-document summarization is to create a summary from a set of documents that answers the information requirements expressed in the query. compared to generic summarization, queryfocused summarization requires the summary biased to a specific query besides the general requirement for a summary. in contrast to the task of question answering (qa) that mainly focuses on simple factoid questions and results in precise answers such as person, location or date, etc., in the case of query-focused summarization, the queries are mostly real-world complex questions and the information provided by the queries is insufficient. so how to understand the query and expand the query has a certain impact on the quality of the query-oriented summary. currently most query oriented abstract systems are based on general summarization systems. by incorporating features that are related to the given query (e.g., the relevance of a sentence to the query), a generic summarization system can be adapted to a query-focused one. due to the limit of information that a query can express, some systems also expand the query using some external resources such as wordnet, by which the synonyms of the query words can be obtained as expansion words @xcite . however, such approaches to query expansion are restricted in query itself and external resources, because they cannot be applied to words not in wordnet such as named entities which frequently occur in queries, and much context related information cannot be captured by only synonyms. lin @xcite uses graph-based sentence ranking and sentence-to-word relationships to implement query expansion, which can improve the effect of graph ranking. however, the paper uses a single query expansion method and only adopts textrank instead of manifold ranking as graph ranking algorithm. so we improved the query expansion method based on the above problems. the main contributions of this work are: our experiments on the dataset duc 2006 and duc 2007 show that the summarization results with query expansion are much better than that without query expansion, achieving the state-of-the-art performance. ## related work graph-based ranking algorithms have been successfully used in text summarization. @xcite proposed lexrank for generic text summarization. they construct a connected similarity graph where nodes represent sentences and edges represent cosine similarities between sentences. a random walk is applied on the graph until converging to a stationary distribution, by which the sentences can be ranked. topic-sensitive lexrank @xcite has been applied to the task of query-focused summarization where the relevance of a sentence to the query is taken into account when performing random walk. @xcite proposed a hypergraph based vertex-reinforced random walk framework for multi-document summarization. @xcite applied a manifold ranking algorithm to query-focused summarization which can simultaneously make full use of both the relationships among all the sentences in the documents and the relationships between the given query and the sentences. regarding manifold ranking algorithms, subsequent researchers have made a lot of improvements. @xcite introduced the matrix factorization model on the basis of xiaojun wan's model, and constructed the sentence relational matrix by using the cosine value of the decomposed matrix. @xcite used the syntactic and shallow semantic kernels to calculate the correlation between sentences based on the model of xiaojun wan. on the basis of xiaojun wan's model, @xcite improves the relationship matrix by mixing the cosine similarity of decomposed matrix and tf-isf cosine similarity in a certain proportion. at the same time, this paper uses lifelong learning topic model to enhance the cooccurrence effect of matrix factorization words. finally, this paper adds statistical features, mix various scoring methods together, and then extract summaries with the strategy of de-redundancy. @xcite not only considers the internal relevance propagation within the sentence set (or within the theme cluster set), but also considers the mutual reinforcement between the ranking of the sentence set and the ranking of the cluster set. these models mainly focus on the calculation of sentence similarity and sentence ranking, but pay little attention to queries. query understanding is a research hotspot in the field of search engines. @xcite proposed the use of a corpus to expand the original query. @xcite proposed a wikipedia-based user query intent understanding. @xcite considered the distribution of expansions in documents, statistical associations with query terms, and semantic associations. @xcite and @xcite combined query expansion with word meaning elimination to enhance the validity of the query. to the best of our knowledge, few people have done the research on the effect of query understanding and query expansion on manifold ranking. therefore, based on the above research, we use multiple query expansion methods to improve the effect of model based on manifold ranking. ## method in this section, this paper will introduce the model architecture, as fig. 1 shows. for the preprocessing step, this paper performed sentence segmentation, word segmentation, word stemming and tf-isf calculation for each sentence in the documents and the query. this paper utilized titles and narratives as queries. when computing the sentence similarities, stop words, emails, digital characters, punctuation marks and single characters were removed, and only the content words (i.e., noun, verb, adjective and adverb) were used after the morphological analysis based on treetagger. for the extraction process of automatic text summarization, this paper selects the method used by @xcite . manifold ranking, query expansion and sentence similarity calculation and will be introduced in detail in following sections. ## conclusion in this paper, we propose a query-oriented multi-document summarization model based on query expansion and manifold ranking. we use wordnet, mean, variance, textrank to expand query. we also use the cosine values of tf-isf, word overlap values and the relative position of sentences to calculate the relation between sentences. we applied our model to duc2006 and duc2007, and found that our model is better than one that combines a subset of query expansion methods and achieves competitive performance with the state-of-the-art results.