Text Reuse Research Articles

Comparing and combining Content‐ and Citation‐based approaches for plagiarism detection

The vast amount of scientific publications available online makes it easier for students and researchers to reuse text from other authors and makes it harder for checking the originality of a given text. Reusing text without crediting the original authors is considered plagiarism. A number of studies have reported the prevalence of plagiarism in academia. As a consequence, numerous institutions and researchers are dedicated to devising systems to automate the process of checking for plagiarism. This work focuses on the problem of detecting text reuse in scientific papers. The contributions of this paper are twofold: (a) we survey the existing approaches for plagiarism detection based on content, based on content and structure, and based on citations and references; and (b) we compare content and citation‐based approaches with the goal of evaluating whether they are complementary and if their combination can improve the quality of the detection. We carry out experiments with real data sets of scientific papers and concluded that a combination of the methods can be beneficial.

Query Formulation for Heuristic Retrieval in Obfuscated and Translated Partially Derived Text

Pre-retrieval query formulation is an important step for identifying local text reuse. Local reuse with high obfuscation, paraphrasing, and translation poses a challenge of finding the reused text in a document. In this paper, three pre-retrieval query formulation strategies for heuristic retrieval in case of low obfuscated, high obfuscated, and translated text are studied. The strategies used are (a) Query formulation using proper nouns; (b) Query formulation using unique words (Hapax); and (c) Query formulation using most frequent words. Whereas in case of low and high obfuscation and simulated paraphrasing, keywords with Hapax proved to be slightly more efficient, initial results indicate that the simple strategy of query formulation using proper nouns gives promising results and may prove better in reducing the size of the corpus for post processing, for identifying local text reuse in case of obfuscated and translated text reuse.

Tracing the Flow of Policy Ideas in Legislatures: A Text Reuse Approach

This article proposes a new approach to investigating the substance of lawmaking. Only a very small proportion of bills become law in the U.S. Congress. However, the bills that do become law often serve as vehicles for language originating in other bills. We investigate “text reuse” methods as a means for tracing the progress of policy ideas in legislation. We then show how a focus on policy ideas leads to new insights into the lawmaking process. Although our focus is on relating content found within bills, the same methods can be used to study policy substance across many research domains.

Patterns of text reuse in a scientific corpus

We consider the incidence of text "reuse" by researchers via a systematic pairwise comparison of the text content of all articles deposited to arXiv.org from 1991 to 2012. We measure the global frequencies of three classes of text reuse and measure how chronic text reuse is distributed among authors in the dataset. We infer a baseline for accepted practice, perhaps surprisingly permissive compared with other societal contexts, and a clearly delineated set of aberrant authors. We find a negative correlation between the amount of reused text in an article and its influence, as measured by subsequent citations. Finally, we consider the distribution of countries of origin of articles containing large amounts of reused text.

Boosting paraphrase detection through textual similarity metrics with abductive networks

A number of metrics have been proposed in the literature to measure text re-use between pairs of sentences or short passages. These individual metrics fail to reliably detect paraphrasing or semantic equivalence between sentences, due to the subjectivity and complexity of the task, even for human beings. This paper analyzes a set of five simple but weak lexical metrics for measuring textual similarity and presents a novel paraphrase detector with improved accuracy based on abductive machine learning. The objective here is 2-fold. First, the performance of each individual metric is boosted through the abductive learning paradigm. Second, we investigate the use of decision-level and feature-level information fusion via abductive networks to obtain a more reliable composite metric for additional performance enhancement. Several experiments were conducted using two benchmark corpora and the optimal abductive models were compared with other approaches. Results demonstrate that applying abductive learning has significantly improved the results of individual metrics and further improvement was achieved through fusion. Moreover, building simple models of polynomial functional elements that identify and integrate the smallest subset of relevant metrics yielded better results than those obtained from the support vector machine classifiers utilizing the same datasets and considered metrics. The results were also comparable to the best result reported in the literature even with larger number of more powerful features and/or using more computationally intensive techniques.

The sense of a connection: Automatic tracing of intertextuality by meaning

In literary study, intertextuality refers to the reuse of text, where new meaning or novel stylistic effects have been generated. Most typically in the digital humanities, algorithms for intertextual analysis search for approximate lexical correspondence that can be described as paraphrase. In this article, we look at a complimentary approach that more closely captures the behavior of the reader when faced with meaningful connections between texts in the absence of words that have the same form or stem, which constrains the match to semantics. The technique we employ for identifying such semantic intertextuality is the popular natural language processing strategy of semantic analysis. Unlike the typical scenario for semantic analysis, where a corpus of long form documents is available, we examine the far more limited textual fragments that embody intertextuality. We are primarily concerned with texts from antiquity, where small phrases or passages often form the locus of comparison. In this vein, we look at a specific case study of established parallels between book 1 of Lucan’s Civil War and all of Vergil’s Aeneid. Applying semantic analysis over these texts, we are able to recover parallels that lexical matching cannot, as well as discover new and interesting thematic matches between the two works.

Copyright Issues Related to the Implementation of Open Access Policies

The presentation will provide an overview of the copyright issues related with the implementation of Open Access policies. It will focus on the need to obtain permission to reproduce and disseminate a copy of any published paper taking into account any copyright transfer signed by authors. This permission is needed to implement Green Open Access policies through repositories. Moreover it will explore the use of open content licenses in repositories and journals to move to the Gold Open Access model that offers not only free access to full text but full reuse of contributions.

Historical Relevance Feedback Detection by Text Re-use Networks

Text re-use has been in the humanist's interest for centuries. Collecting parallel texts implies giving a certain information, e.g. a moral statement or report on wars and conflicts, a kind of witness. The more independent parallel texts are collected, the more feasible the information is. The contribution reported here is on automatic detection of text re-use and the usage of a text re-use network to derive a Cultural Heritage Aware PageRank technique given ancient text re-uses like quotations, paraphrases, and allusions.

Paraphrase acquisition via crowdsourcing and machine learning

To paraphrase means to rewrite content while preserving the original meaning. Paraphrasing is important in fields such as text reuse in journalism, anonymizing work, and improving the quality of customer-written reviews. This article contributes to paraphrase acquisition and focuses on two aspects that are not addressed by current research: (1) acquisition via crowdsourcing, and (2) acquisition of passage-level samples. The challenge of the first aspect is automatic quality assurance; without such a means the crowdsourcing paradigm is not effective, and without crowdsourcing the creation of test corpora is unacceptably expensive for realistic order of magnitudes. The second aspect addresses the deficit that most of the previous work in generating and evaluating paraphrases has been conducted using sentence-level paraphrases or shorter; these short-sample analyses are limited in terms of application to plagiarism detection, for example. We present the Webis Crowd Paraphrase Corpus 2011 (Webis-CPC-11), which recently formed part of the PAN 2010 international plagiarism detection competition. This corpus comprises passage-level paraphrases with 4067 positive samples and 3792 negative samples that failed our criteria, using Amazon's Mechanical Turk for crowdsourcing. In this article, we review the lessons learned at PAN 2010, and explain in detail the method used to construct the corpus. The empirical contributions include machine learning experiments to explore if passage-level paraphrases can be identified in a two-class classification problem using paraphrase similarity features, and we find that a k-nearest-neighbor classifier can correctly distinguish between paraphrased and nonparaphrased samples with 0.980 precision at 0.523 recall. This result implies that just under half of our samples must be discarded (remaining 0.477 fraction), but our cost analysis shows that the automation we introduce results in a 18% financial saving and over 100 hours of time returned to the researchers when repeating a similar corpus design. On the other hand, when building an unrelated corpus requiring, say, 25% training data for the automated component, we show that the financial outcome is cost neutral, while still returning over 70 hours of time to the researchers. The work presented here is the first to join the paraphrasing and plagiarism communities.

Never Coming to a Theatre near You: Recut Film Trailers

In the lead-up to the release of some feature films, fake and fan trailers are created by users and uploaded to YouTube and other Web sites. These trailers demonstrate that users are literate not only in the form of the trailer itself, but also in the Hollywood system and how it markets products to audiences. Circulating in a networked environment online, these texts, which play with the form of the trailer, perform and embody users' and fans' desire to see not just the feature film but also the official trailer itself. I discuss these fake and fan trailers in relation to cinematic anticipation and describe how they navigate both spatial and temporal bounds. Using the architectural concept of the desire line, I argue that spatial frameworks can be usefully employed to consider how users navigate online spaces, media, and concepts through the form of the trailer.

When Does Previous Disclosure Become a “Prior Publication” Problem?

How much of the substance of a paper can be made public before a peer-reviewed biomedical journal will consider the paper no longer sufficiently newsworthy for publication? We are talking here about where there has been a “prior publication” somewhere other than in another peer-reviewed journal. The considerations are different from publication that is duplicate/redundant/salami/repetitive/overlapping/multiple—all terms referring to where a paper, part of it, or its data have been published in another peer-reviewed journal. 1 Langdon-Neuner E Publication more than once: duplicate publication and reuse of text. J Tehran University Heart Center. 2008; 3: 1-4 Google Scholar Erratum: Chest 2009; 135:233–237CHESTVol. 135Issue 6PreviewIn the January 2009 issue, in the article by Langdon-Neuner titled “When Does Previous Disclosure Become a Prior Publication?” (Chest 2009; 135:233–237), the last sentence of the first full paragraph on page 234, the sentence should read, “Press reports of meetings are also not precluded, but additional data or copies of tables should not amplify such reports.” Full-Text PDF

The Bottom Line: Does Text Reuse Translate into Gains in Productivity?

The Bottom Line: Does Text Reuse Translate into Gains in Productivity?

Re-Using Text from One's Own Previously Published Papers: An Exploratory Study of Potential Self-Plagiarism

A preliminary, two-part study explored the extent to which authors reuse portions of their own text from previously published papers. All 9 articles from a recent issue of a psychology journal were selected as target papers. Up to 3 of the most recent references cited in each of the target articles and written by the same authors were also obtained. All target articles and their corresponding references were stored digitally. Then, using specialized software, each reference was compared to its target article to assess the number of strings of text identical to both papers. Only one of the nine target articles reused significant amounts of text from one of its references. To explore further the possibility of additional text reuse, the references in each of the 9 sets of papers were compared against each other. The new comparison identified 5 pairs of papers with a substantial number of identical strings of text of 6 consecutive words in length or longer, but most of the reused text was confined to the Method section. The results suggest that some of these authors reuse their own text with some frequency, but this was largely confined to complex methodological descriptions of a research design and procedure.