ai citation generator

ai citation generator

AI Citation Generator

1. Introduction

To the best of our knowledge, despite the increasing number and popularity of tools, gadgets, and online service applications that support academic and R&D activities, no works available have directed their attention to a citation generation tool which benefits the NLP and AI community, especially considering the untapped potential of a citation generator with a paraphrasing feature. This work describes the design and justification, as well as the implementation and evaluation, of a well-designed automatic citation generator that is expected to generate highly accurate and tightly-coupled bibliography entries. Our solution first attempts to resemble and compare the given text with a seed set of candidate papers and tries to utilize the additional data to obtain rough source location information for the in-text citation. This system also incorporates MITIE, an open-source information extraction system for identifying references for the author. Future work will involve large-scale user studies in order to protect our results from its limitation, due to its balanced precision and recall of the cite sentence advancement stages.

Citation generator has long been an important tool for academic activities because it helps authors to generate references more easily and accurately. Even so, existing citation generator tools, such as Mendeley and Endnote, present some limitations. As an example, you probably will not find an official citation style from certain reputable conferences such as Supercomputing and Foundations of Software Engineering. Additionally, no citation generator tool offers a painless paraphrasing function.

2. Benefits of an AI Citation Generator

Despite their benefits, the work of citing sources is disliked, albeit to different degrees, by many. The small minority who enjoy it acknowledge its time and concentration requirement. For example, with the APA source-specific rules and variations by universities, a student researcher once noted the presence of significant stress after they accidentally broke a previously effective citation pipeline! Tasks with guidelines and rules inevitably have many “edges” to manage! Project Gutenberg wishes to help reduce this dislike by demonstrating the efficacy of AI controlled by species such as Python for citing academic papers. As the motivation for this availability, we also found text-mining relevant educational resources belonging to Springer Nature easily accessible via their conference API. The success of our endeavor adds to data in the dashboard describing the potential for AI expansion. Along the way, we developed a Python script that may assist early researchers as they build essential skills. The fact that the Redmond Conference API, the main source of the training bigrams eventually used for GPT-2-small, was, for the most part, not in a position to include APA citations among its APIs shows how vital extra data, other resources, and innovation are in democratizing opportunities for personal and institutional students.

An AI citation generator will be used to fuel the process of citing sources. It does the heavy lifting by typing out citations for many different types of sources. This is different from traditional citation generators, which have only done so for academic journals, a limited number of citation styles, and a limited number of supported languages. Beyond their benefits of finding and fixing errors in citations manually typed out by students, compliant citations produced are important to academic research and publishing. To be specific, they are used to credit past contributors whose work informed the research presented in a paper. They include a recognizable “trail” of sources for others who are interested in the research presented. They inform academic peers about the sponsorship that furthered the researcher’s work. Perhaps most importantly, they are the sources referred to by academicians, both to confirm other presentations’ sources are credible and in follow-up research.

3. How an AI Citation Generator Works

Researchers have pointed out that the two main subcognitive functions of a citation generator are to (i) automatically, using a grammar-like model and machine learning techniques, create recommendations for a cocultural apt text feature, and (ii) automatically, using similar techniques, functionalize the recommendation. In a pioneer to democratize scholarship, scientists have developed a general bibliography tool that helps scholar researchers automatically make and reference paper references. The tool is ideal for college students and other users who need to prepare essays, term papers, theses, and dissertations. Researchers claim that the tool automates the generation of reference notices, which allows scientists to focus on text facets that are crucial to their work.

An AI or machine learning-based citation generator is a cloud-based software that builds a bibliography for academic papers, essays, and various types of academic work. The user inputs the notion, URL, subject, and hits return, and the AI generates a citation. Users are likely initiated into two models, one for creating recommendations and one for the algorithm of producing references. The first module includes an NLP model that draws article excerpts using the excerpt, extract, encapsulate, assemble, explain, or EEEA pipeline. The article generator employs the deep learning approach of Long Short-term Memories, or LSTM, to generate references based on the user-entered notion. Based on former generations of references, the users input into the module a notion and a URL, from which an Internet scraper retrieves article excerpts to form the idea of a novel excerpt. This positively describes the notion and is organized as the concept-feature sequence of the modeled language.

4. Features and Functionality

In this work, we did not use clustered annotations or citation context (sentences in which citations appear). The clustered annotations are related to the possibility of having more than one annotation for a given candidate string, provided that some specific condition is met. The context of citations is important for different disambiguation tasks, such as distinguishing if one paper is related or not to another paper because of certain evidence. The context should be considered as an additional feature to bring some enhancements to the “relevance by top k” step, where k is the number of top subsequent citations made by a target remaining to be validated. Also, the context of citing variables should be considered in resolving the problem of strings that occur frequently and could confuse the algorithm.

In one of the future research directions, we plan to enable users to generate citations in different formats that are commonly used. For example, the clearing house could allow users to manually edit citation text. Users could cite documents in different languages, and the system should successfully process documents with English translations of the original citations as well. Names and documents could be searched with advanced features. The user could search in a specific journal or in documents of a specific author, etc. Moreover, the clearing house is considering the possibility to implement different citation styles.

After the login, the user has the possibility to generate the citation by uploading the PDF document. The document is analyzed and the citations are provided in the original text format. The user can add their own comments to the generated citation and then finalize the citation to be saved in the database. The final citation can be provided to the user in three formats: .bib, .ris, and conaps. We used the provided citation as training data for the manually annotated data, which we will describe in the evaluation section.

5. Conclusion

These results are interesting for several reasons. For one, given that many other AI systems exist for tasks such as authoring, peer review, and as part of the paper planning process, the contribution offered by developing an AI-based system which focuses only on this one aspect of paper generation could be quite substantial. Furthermore, as the first step towards developing any such tool, this work fills a gap in the study of AI publications and science generators and their impact on writing and the authoring process. Lastly, in a larger context, AI literature generation systems like JacoGran or arXiv Vanity have created considerable debate surrounding the authenticity of the authorship of such publications or whether the community could be getting more from these AIs. The more that we rigorously study and understand how AI merges more seamlessly with authoring practices, the better informed we will be to ensure these models contribute meaningfully to the generation of scientific knowledge instead of solely the documentation of these claims.

In this paper, we set out to understand if and how AI starts playing a role in scientific paper composition and more specifically, citation organization. We developed a citation generator based on the BERT model and compared it with a rule-based system and humans. We found that while BERT and humans performed similarly in certain tasks such as identifying whether a citation should be included in a paper or not, the two systems dealt with other aspects rather differently. The results show promise for developing AI citation generation tools that could complement authoring, copyediting, and other production processes.