varunr14/newsummary huggingface dataset:
Nowadays a large number of opinion reviews are posted on the Web . Such reviews are a very important source of information for customers and companies . The former rely more than ever on online reviews to make their purchase decisions and the latter to respond promptly to their clients expectations . Unfortunately due to the business that is behind there is an increasing number of deceptive opinions that is fictitious opinions that have been deliberately written to sound authentic in order to deceive the consumers promoting a low quality product or criticizing a potentially good quality one . In this paper we focus on the detection of both types of deceptive opinions positive and negative . Due to the scarcity of examples of deceptive opinions we propose to approach the problem of the detection of deceptive opinions employing PU learning . PU learning is a semi supervised technique for building a binary classifier on the basis of positive and unlabeled examples only . Concretely we propose a novel method that with respect to its original version is much more conservative at the moment of selecting the negative examples from the unlabeled ones . The obtained results show that the proposed PU learning method consistently outperformed the original PU learning approach . In particular results show an average improvement of 8.2 and 1.6 over the original approach in the detection of positive and negative deceptive opinions respectively . 


Summary: Detection of negative deceptive opinion spam. Improved PU learning approach. Compares the performance of the proposed approach and the original PU learning method. The role of opinions polarity in the detection of deception. Reports experimental results on a set of negative deceptive opinions.
Transductive classification is a useful way to classify texts when labeled training examples are insufficient . Several algorithms to perform transductive classification considering text collections represented in a vector space model have been proposed . However the use of these algorithms is unfeasible in practical applications due to the independence assumption among instances or terms and the drawbacks of these algorithms . Network based algorithms come up to avoid the drawbacks of the algorithms based on vector space model and to improve transductive classification . Networks are mostly used for label propagation in which some labeled objects propagate their labels to other objects through the network connections . Bipartite networks are useful to represent text collections as networks and perform label propagation . The generation of this type of network avoids requirements such as collections with hyperlinks or citations computation of similarities among all texts in the collection as well as the setup of a number of parameters . In a bipartite heterogeneous network objects correspond to documents and terms and the connections are given by the occurrences of terms in documents . The label propagation is performed from documents to terms and then from terms to documents iteratively . Nevertheless instead of using terms just as means of label propagation in this article we propose the use of the bipartite network structure to define the relevance scores of terms for classes through an optimization process and then propagate these relevance scores to define labels for unlabeled documents . The new document labels are used to redefine the relevance scores of terms which consequently redefine the labels of unlabeled documents in an iterative process . We demonstrated that the proposed approach surpasses the algorithms for transductive classification based on vector space model or networks . Moreover we demonstrated that the proposed algorithm effectively makes use of unlabeled documents to improve classification and it is faster than other transductive algorithms . 


Summary: Scalable algorithm based on bipartite networks to perform transduction. Unlabeled data effectively employed to improve classification performance. Better performance than algorithms based on vector space model or networks. Rigorous evaluation to show the drawbacks of the existing transductive algorithms. Trade off analysis between inductive supervised and transductive classification.
We consider a consignment contract with consumer non defective returns behavior . In our model an upstream vendor contracts with a downstream retailer . The vendor decides his consignment price charged to the retailer for each unit sold and his refund price for each returned item and then the retailer sets her retail price for selling the product . The vendor gets paid based on net sold units and salvages unsold units as well as returned items in a secondary market . Under the framework we study and compare two different consignment arrangements the retailer vendor manages consignment inventory programs . To study the impact of return policy we discuss a consignment contract without return policy as a benchmark . We show that whether or not the vendor offers a return policy it is always beneficial for the channel to delegate the inventory decision to the vendor . We find that the vendor s return policy depends crucially on the salvage value of returns . If the product has no salvage value the vendor s optimal decision is not to offer a return policy otherwise the vendor can gain more profit by offering a return policy when the salvage value turns out to be positive . 


Summary: To study a general model considering consumer uncertain post purchase valuation. To explore inventory control problem under consignment contract with consumer returns. To propose optimal inventory control strategy characterizing vendor s return policy.
In the Prize Collecting Steiner Tree Problem we are given a set of customers with potential revenues and a set of possible links connecting these customers with fixed installation costs . The goal is to decide which customers to connect into a tree structure so that the sum of the link costs plus the revenues of the customers that are left out is minimized . The problem as well as some of its variants is used to model a wide range of applications in telecommunications gas distribution networks protein protein interaction networks or image segmentation . In many applications it is unrealistic to assume that the revenues or the installation costs are known in advance . In this paper we consider the well known Bertsimas and Sim robust optimization approach in which the input parameters are subject to interval uncertainty and the level of robustness is controlled by introducing a control parameter which represents the perception of the decision maker regarding the number of uncertain elements that will present an adverse behavior . We propose branch and cut approaches to solve the robust counterparts of the PCStT and the Budget Constraint variant and provide an extensive computational study on a set of benchmark instances that are adapted from the deterministic PCStT inputs . We show how the Price of Robustness influences the cost of the solutions and the algorithmic performance . Finally we adapt our recent theoretical results regarding algorithms for a general class of B S robust optimization problems for the robust PCStT and its budget and quota constrained variants . 


Summary: The paper deals with the Robust PCStT addressing interval uncertainty on its data. Different mathematical programming formulations are proposed. Branch and cut Algorithms are designed and extensively tested. Recent theoretical results are adapted for the Robust PCStT and some of it variants.
Document filtering is a popular task in information retrieval . A stream of documents arriving over time is filtered for documents relevant to a set of topics . The distinguishing feature of document filtering is the temporal aspect introduced by the stream of documents . Document filtering systems up to now have been evaluated in terms of traditional metrics like precision recall MAP nDCG F1 and utility . We argue that these metrics do not capture all relevant aspects of the systems being evaluated . In particular they lack support for the temporal dimension of the task . We propose a time sensitive way of measuring performance of document filtering systems over time by employing trend estimation . In short the performance is calculated for batches a trend line is fitted to the results and the estimated performance of systems at the end of the evaluation period is used to compare systems . We detail the application of our proposed trend estimation framework and examine the assumptions that need to hold for valid significance testing . Additionally we analyze the requirements a document filtering metric has to meet and show that traditional macro averaged true positive based metrics like precision recall and utility fail to capture essential information when applied in a batch setting . In particular false positives returned in a batch for topics that are absent from the ground truth in that batch go unnoticed . This is a serious flaw as over generation of a system might be overlooked this way . We propose a new metric aptness that does capture false positives . We incorporate this metric in an overall score and show that this new score does meet all requirements . To demonstrate the results of our proposed evaluation methodology we analyze the runs submitted to the two most recent editions of a document filtering evaluation campaign . We re evaluate the runs submitted to the Cumulative Citation Recommendation task of the 2012 and 2013 editions of the TREC Knowledge Base Acceleration track and show that important new insights emerge . 


Summary: We propose a new way of measuring document filtering system performance over time. Performance is calculated per batch and a trend line is fitted to the results. Systems are compared by their performance at the end of the evaluation period. Important insights emerge by re evaluating TREC KBA CCR runs of 2012 and 2013.
This study proposes a new 4D framework for citation distribution analysis . The importance and differences in the breadth and depth of citation distribution are analyzed . Easily computable indices X Y and XY are proposed which provide estimates of the breadth and depth of citation distribution . A knowledge unit can be an article author institution journal or a set of something . Index X which represents the breadth of citation distribution is the number of different knowledge units that cite special knowledge units . Index Y which represents the depth of citation distribution is the maximum number of citations among several knowledge units that refer to specific knowledge units . Index XY which synthetically represents Indices X and Y the feature and focus impacts of a knowledge unit is index X divided by index Y . We analyze empirically the citation and reference distributions of 84 journals from the Information science and library science category of the Journal Citation Reports at the journal to journal level . Indices X Y and XY reflect the actual breadth and depth of citation distribution . Differences exist among Indices X Y and XY . Differences also exist between these indices and other bibliometric indicators . These indices can not be replaced by existing bibliometric indicators . Specifically the absolute values of indices X and Y are good supplements to existing bibliometric indicators . However index XY and the relative values of Indices X and Y represent new aspects of bibliometric indicators . 


Summary: 4D framework for citation distribution analysis are proposed. Easily computable indices X Y and XY are proposed. The index X represents the breadth of citation distribution. The index Y represents the depth of citation distribution. The index XY synthetically represents indices X and Y.
The absence of diacritics in text documents or search queries is a serious problem for Turkish information retrieval because it creates homographic ambiguity . Thus the inappropriate handling of diacritics reduces the retrieval performance in search engines . A straightforward solution to this problem is to normalize tokens by replacing diacritic characters with their American Standard Code for Information Interchange counterparts . However this so called ASCIIfication produces either synthetic words that are not legitimate Turkish words or legitimate words with meanings that are completely different from those of the original words . These non valid synthetic words can not be processed by morphological analysis components which expect the input to be valid Turkish words . By contrast synthetic words are not a problem when no stemmer or a simple first n characters stemmer is used in the text analysis pipeline . This difference emphasizes the notion of the diacritic sensitivity of stemmers . In this study we propose and evaluate an alternative solution based on the application of deASCIIfication which restores accented letters in query terms or text documents . Our risk sensitive evaluation results showed that the diacritics restoration approach yielded more effective and robust results compared with normalizing tokens to remove diacritics . 


Summary: Risk sensitive evaluation of approaches for handling diacritics in Turkish information retrieval. Application of diacritics restoration to Turkish information retrieval. Investigation of the diacritics sensitivity of stemming algorithms.
The International Classification of Diseases is a type of meta data found in many Electronic Patient Records . Research to explore the utility of these codes in medical Information Retrieval applications is new and many areas of investigation remain including the question of how reliable the assignment of the codes has been . This paper proposes two uses of the ICD codes in two different contexts of search Pseudo Relevance Judgments and Pseudo Relevance Feedback . We find that our approach to evaluate the TREC challenge runs using simulated relevance judgments has a positive correlation with the TREC official results and our proposed technique for performing PRF based on the ICD codes significantly outperforms a traditional PRF approach . The results are found to be consistent over the two years of queries from the TREC medical test collection . 


Summary: A new approach using ICD codes to substitute the manual relevance judgements. Improving IR effectiveness by using the proposed ICD based query expansion. A novel method to automatically map clinical queries into ICD codes.
We use extractive multi document summarization techniques to perform complex question answering and formulate it as a reinforcement learning problem . Given a set of complex questions a list of relevant documents per question and the corresponding human generated summaries as training data the reinforcement learning module iteratively learns a number of feature weights in order to facilitate the automatic generation of summaries i.e . answers to previously unseen complex questions . A reward function is used to measure the similarities between the candidate summary sentences and the abstract summaries . In the training stage the learner iteratively selects the important document sentences to be included in the candidate summary analyzes the reward function and updates the related feature weights accordingly . The final weights are used to generate summaries as answers to unseen complex questions in the testing stage . Evaluation results show the effectiveness of our system . We also incorporate user interaction into the reinforcement learner to guide the candidate summary sentence selection process . Experiments reveal the positive impact of the user interaction component on the reinforcement learning framework . 


Summary: Reinforcement learning formulation for complex question answering. Abstract summaries used for small amount of supervision using reward scores. User interaction component incorporated to guide candidate sentence selection. Experiments reveal that systems trained with user interaction perform better. The reinforcement system is able to learn automatically and effectively.
We present a novel generic programming implementation of a column generation algorithm for the generalized staff rostering problem . The problem is represented as a generalized set partitioning model which is able to capture commonly occurring problem characteristics given in the literature . Columns of the set partitioning problem are generated dynamically by solving a pricing subproblem and constraint branching in a branch and bound framework is used to enforce integrality . The pricing problem is formulated as a novel three stage nested shortest path problem with resource constraints that exploits the inherent problem structure . A very efficient implementation of this pricing problem is achieved by using generic programming principles in which careful use of the C pre processor allows the generator to be customized for the target problem at compile time . As well as decreasing run times this new approach creates a more flexible modeling framework that is well suited to handling the variety of problems found in staff rostering . Comparison with a more standard run time customization approach shows that speedups of around a factor of 20 are achieved using our new approach . The adaption to a new problem is simple and the implementation is automatically adjusted internally according to the new definition . We present results for three practical rostering problems . The approach captures all features of each problem and is able to provide high quality solutions in less than 15minutes . In two of the three instances the optimal solution is found within this time frame . 


Summary: We describe a nested column generation algorithm for staff rostering. We show how generic programming can be used within a column generation implementation. Our resulting system is faster and more flexible than traditional systems. We can obtain good solutions to practical staff rostering problems.
Participatory budgets are becoming increasingly popular in many municipalities all around the world . The underlying idea is to allow citizens to participate in the allocation of a municipal budget . Many advantages have been suggested for such experiences including legitimization and more informed and transparent decisions . There are many conceivable variants of such processes . However in most cases both its design and implementation are carried out in an informal way . In this paper we propose a methodology to design a participatory budget process based on a multicriteria decision making model . 


Summary: We propose a methodology to design a participatory budget processes. This methodology is based on a multicriteria decision making model. We have identified alternatives constraints criteria and a value function model. We illustrate the use of the methodology with an example. With this approach we hope to facilitate the design of participatory budget processes.
We present IntoNews a system to match online news articles with spoken news from a television newscasts represented by closed captions . We formalize the news matching problem as two independent tasks closed captions segmentation and news retrieval . The system segments closed captions by using a windowing scheme sliding or tumbling window . Next it uses each segment to build a query by extracting representative terms . The query is used to retrieve previously indexed news articles from a search engine . To detect when a new article should be surfaced the system compares the set of retrieved articles with the previously retrieved one . The intuition is that if the difference between these sets is large enough it is likely that the topic of the newscast currently on air has changed and a new article should be displayed to the user . In order to evaluate IntoNews we build a test collection using data coming from a second screen application and a major online news aggregator . The dataset is manually segmented and annotated by expert assessors and used as our ground truth . It is freely available for download through the Webscope program . http webscope.sandbox.yahoo.com . Our evaluation is based on a set of novel time relevance metrics that take into account three different aspects of the problem at hand precision timeliness and coverage . We compare our algorithms against the best method previously proposed in literature for this problem . Experiments show the trade offs involved among precision timeliness and coverage of the airing news . Our best method is four times more accurate than the baseline . 


Summary: IntoNews is an Intonow app for matching web news with news broadcasted on TV. We present a framework that models the task as 2 separate sub tasks Find retrieve. We design and publicly provide an evaluation testbed for the IntoNews problem. Quality evaluation has to trade off 2 competing metrics coverage and precision.
Every item produced transported used and discarded within a Supply Chain generates costs and creates an impact on the environment . The increase of forward flows as effects of market globalization and reverse flows due to legislation warranty recycling and disposal activities affect the ability of a modern SC to be economically and environmentally sustainable . In this context the study considers an innovative sustainable closed loop SC problem . It first introduces a linear programming model that aims to minimize the total SC costs . Environmental sustainability is guaranteed by the complete reprocessing of an end of life product the re use of components the disposal of unusable parts sent directly from the manufacturers with a closed loop transportation system that maximizes transportation efficiency . Secondly the authors consider the problem by means of a parametrical study by analyzing the economical sustainability of the proposed CLSC model versus the classical Forward Supply Chain model from two perspectives Case 1 the traditional company perspective where the SC ends at the customers and the disposal costs are not included in the SC and Case 2 the social responsibility company perspective where the disposal costs are considered within the SC . The relative impact of the different variables in the SC structure and the applicability of the proposed model in terms of total costs SC structure and social responsibility are investigated thoroughly and the results are reported at the conclusion of the paper . 


Summary: Environmental sustainability through the reprocessing of end of life products. Traditional perspective and social responsibility perspective analysis. Critical aspects for sustainability in supply chain design are analysis. Transportation efficiency maximization through closed loop transportation system.
In this paper we present an efficient spectral clustering method for large scale data sets given a set of pairwise constraints . Our contribution is threefold clustering accuracy is increased by injecting prior knowledge of the data points constraints to a small affinity submatrix connected components are identified automatically based on the data points pairwise constraints generating thus isolated islands of points furthermore local neighborhoods of points of the same connected component are adapted dynamically and constraints propagation is performed so as to further increase the clustering accuracy finally the complexity is preserved low by following a sparse coding strategy of a landmark spectral clustering . In our experiments with three benchmark shape face and handwritten digit image data sets we show that the proposed method outperforms competitive spectral clustering methods that either follow semi supervised or scalable strategies . 


Summary: We face the real world problem of having a limited set of pairwise constraints. Using pairwise constraints connected components CC are generated. The points local neighborhoods of the same CC are dynamically adapted. Constraints propagation to CC neighborhoods to increase the clustering accuracy. Scalability is ensured by following a landmark strategy.