An unsupervised approach to generating generic summaries of documents

We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.     

On recommendation problems beyond points of interest

Recommendation systems aim to recommend items or packages of items that are likely to be of interest to users. Previous work on recommendation systems has mostly focused on recommending points of interest (POI), to identify and suggest top-k items or packages that meet selection criteria and satisfy compatibility constraints on items in a package, where the (packages of) items are ranked by their usefulness to the users. As opposed to prior work, this paper investigates two issues beyond POI recommendation that are also important to recommendation systems. When there exist no sufficiently many POI that can be recommended, we propose (1) query relaxation recommendation to help users revise their selection criteria, or (2) adjustment recommendation to guide recommendation systems to modify their item collections, such that the users׳ requirements can be satisfied.We study two related problems, to decide (1) whether the query expressing the selection criteria can be relaxed to a limited extent, and (2) whether we can update a bounded number of items, such that the users can get desired recommendations. We establish the upper and lower bounds of these problems, all matching, for both combined and data complexity, when selection criteria and compatibility constraints are expressed in a variety of query languages, for both item recommendation and package recommendation. To understand where the complexity comes from, we also study the impact of variable sizes of packages, compatibility constraints and selection criteria on the analyses of these problems. Our results indicate that in most cases the complexity bounds of query relaxation and adjustment recommendation are comparable to their counterparts of the basic recommendation problem for testing whether a given set of (resp. packages of) items makes top-k items (resp. packages). In other words, extending recommendation systems with the query relaxation and adjustment recommendation functionalities typically does not incur extra overhead.     

GOS+MU:一种查询对象选择新方法

在分析单一MU(Most Uncertainty)采样缺陷的基础上,提出一种"全局最优搜寻"方法 GOS(Global Optimal Search),并结合MU共同完成查询选择。GOS+MU方法中,GOS着眼全局寻找目标,在应用环境能提供的训练样本数量有限、分类器受训不充分时,该方法选择的对象学习价值高,能快速推进分类器学习进程;MU则能够在GOS采样失效情形下,利用分类器当前训练成果,选择查询不确定性最强的样本补充训练集。通过对网络商品的用户评论进行分类仿真,并比较其他采样学习方法的效果,证明了GOS+MU方法在压缩学习成本、提高训练效率方面的有效性。     

Learning keyword phrases from query logs of USPTO patent examiners for automatic query scope limitation in patent searching

Professional search in patent repositories poses several unique challenges. One key requirement is to search the entire affected space of concepts, following well-defined procedures to ensure traceability of results obtained. Several techniques have been introduced to enhance query generation, preferably via automated query term expansion, to improve retrieval effectiveness. Currently, these approaches are mostly limited to computing additional query terms from patent documents based on statistical measures. For conceptual search to solve the limitation of traditional keyword search standard dictionaries are used to provide synonyms and keyword phrases for query refinement. Studies show that these are insufficient in such highly specialized domains. In this paper, we present an approach to extract keyword phrases from query logs created during the validation procedure of the patent applications. This creates valuable domain-specific lexical databases for several specific patent classes that can be used to both expand as well as limit the scope of a patent search. This provides a more powerful means to guide a professional searcher through the search process. We evaluate the lexical databases based on real query sessions of patent examiners.     

Behaviors of concrete-filled steel tubular members subjected to combined loading

The present study is an investigation on the behaviors of concrete-filled thin-walled steel tubular members subjected to combined loading, such as compression and torsion, bending and torsion, compression, bending and torsion. ABAQUS software is used in this paper for the finite element analysis (FEA). A comparison of results calculated using this modeling shows generally good agreement with test results. The FEA modeling is then used to investigate the influence of important parameters that determine the ultimate strength of the composite members under combined loading, such as compression and torsion, bending and torsion, compression, bending and torsion. The parametric studies provide information for the development of formulae for calculating the ultimate strength of the composite members subjected to combined loading.     

Revising threshold functions

A revision algorithm is a learning algorithm that identifies the target concept, starting from an initial concept. Such an algorithm is considered efficient if its complexity (in terms of the resource one is interested in) is polynomial in the syntactic distance between the initial and the target concept, but only polylogarithmic in the number of variables in the universe. We give an efficient revision algorithm in the model of learning with equivalence and membership queries for threshold functions, and some negative results showing, for instance, that threshold functions cannot be revised efficiently from either type of query alone. The algorithms work in a general revision model where both deletion and addition type revision operators are allowed.     

Extension of a combined analytical/numerical initial value problem solver for turbulent mixing with combustion

Here, we describe the development of a reacting flow multi-species/combustion methodology, implemented as an extension to the differential reduced ejector analysis (DREA) computer program [Mathematical and computer modeling, vol. 31, 2000, p. 21; Appl. Math. Model. 25 (2001) 427; Comput. Math. Appl. 43(10–11); NASA Contractor Report, 1998]. Use of the single fluid IVP solver framework that was developed for the original DREA model has been directly coupled into the combustion formulation. With these modifications, the analysis has an elementary single step reaction Fuel+Oxidizer→Product combustion capability. Though approximate in nature, the simplicity and efficiency of the DREA formulation make it suitable for its original niche, namely design and preliminary design environments where more complex and expensive models may be inappropriate.     

Conceptual document modelling and retrieval

This paper presents the main current theoretical issues in Information Retrieval. The principles of conceptual modelling, as they have emerged in the database area, are presented and their application to document modelling in order to enhance document retrieval is discussed. Finally, the main features of the MULTOS project are presented and critically reviewed confronting them with the requirements which have been identified during the general discussion on document conceptual modelling for information retrieval.     

一种复合制导导弹的导航滤波器设计

研究了一种中制导段采用雷达修正的遥控制导方式和末制导段采用捷联式自寻的导引头的自导引制导方式的捷联式复合制导的中远程战术导弹的导航滤波问题，考虑到目标的大机动性和模型的非线性，基于最佳估计理论中的广义卡尔曼滤波理论，建立了通用导航滤波方程，仿真计算与仿真结果表明所做设计是合理和有效的。