Big Scholarly Data in CiteSeerX

Ororbia, Alexander G.; Wu, Jian; Khabsa, Madian; Williams, Kyle; Giles, C. Lee

doi:10.1145/2740908.2741736

Cited by 8 publications

(8 citation statements)

References 30 publications

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“…Fan [16] 147 documents Metadata Färber [17] 90K documents References Grennan [21] 1B references References Saier [51,50] 1M documents. References Ley [30,52] 6M documents Metadata, references Mccallum [39] 935 documents Metadata, references Kyle [34] 8.1M documents Metadata, references Ororbia [43] 6M documents. Metadata, references Bast [6] 12,098 documents Body text, sections, title Li [31] 500K pages Captions, equations, figures, footers lists, metadata, paragraphs, references, sections, tables…”

Section: Labeled Datasets and Prior Benchmarksmentioning

confidence: 99%

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A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents

Meuschke

Jagdale

Spinde

et al. 2023

Lecture Notes in Computer Science

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Extracting information from academic PDF documents is crucial for numerous indexing, retrieval, and analysis use cases. Choosing the best tool to extract specific content elements is difficult because many, technically diverse tools are available, but recent performance benchmarks are rare. Moreover, such benchmarks typically cover only a few content elements like header metadata or bibliographic references and use smaller datasets from specific academic disciplines. We provide a large and diverse evaluation framework that supports more extraction tasks than most related datasets. Our framework builds upon DocBank, a multi-domain dataset of 1.5M annotated content elements extracted from 500K pages of research papers on arXiv. Using the new framework, we benchmark ten freely available tools in extracting document metadata, bibliographic references, tables, and other content elements from academic PDF documents. GROBID achieves the best metadata and reference extraction results, followed by CERMINE and Science Parse. For table extraction, Adobe Extract outperforms other tools, even though the performance is much lower than for other content elements. All tools struggle to extract lists, footers, and equations. We conclude that more research on improving and combining tools is necessary to achieve satisfactory extraction quality for most content elements. Evaluation datasets and frameworks like the one we present support this line of research. We make our data and code publicly available to contribute toward this goal.

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Section: Labeled Datasets and Prior Benchmarksmentioning

confidence: 99%

“…Publications in chronological order; the labels indicate the first author only. 2 (B) Body text, (M) Metadata, (R) References, (RO) Reading order, (ToC) Table of contents 3 Domain-specific datasets: Computer Science: CiteSeer[43], CORA[39], DBLP[52], FLUX-CiM[10,11], ManCreat[42]; Health Science: PubMed[40], PMC[41] …”

mentioning

confidence: 99%

A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents

Meuschke

Jagdale

Spinde

et al. 2023

Lecture Notes in Computer Science

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“…Citation extraction can be performed using ParsCit [92], FLUX-CiM [93] and a CRF-based system [94]. Ororbia et al [47] compared the three methods and concluded that the performance of ParsCit and CRF-based system is comparable. Besides this, it outperforms FLUX-CiM.…”

Section: Citationsmentioning

confidence: 99%

“…Duplicates can be exact duplicates or near-duplicates. In order to eliminate exact duplicates, the SHA1 values of newly extracted documents are matched with that of existing documents and key mapping algorithm can be used for getting rid of near duplicates [47]. The Key Mapping algorithm is also used to align papers to citations.…”

Section: Elimination Of Duplicatesmentioning

confidence: 99%

A survey on scholarly data: From big data perspective

Khan

Liu

Shakil

et al. 2017

Information Processing & Management

117

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With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research. In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems. This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

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“…[14] Khan [15] 4 PDF ParsCit [16,17] CiteSeerX [18] [19] ParsCit FLUX-CiM [20] [21] [22] FLUX-CiM [24] [25] Kikkawa [26] Halfaker [9] [8]…”

mentioning

confidence: 99%

A Method to Identify the Edits Adding Bibliographic References to Wikipedia

Kikkawa

Takaku

Yoshikane

2020

Journal of Japan Society of Information and Knowledge

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Wikipedia DOI 93.3% 22 20 90% We proposed a method to identify the edits adding bibliographic references to Wikipedia. The proposed method consists of the following steps. (1) The method extracts the references and matches them to a bibliographic database to build the basic data set. (2) It obtains the full revision history of the page that includes the references from dump data of Wikipedia. It also extracts identifiers and titles for each reference from the basic data set. (3) The method gets the candidate edits adding the references by using the ways, which use either identifiers or titles. (4) The method selects the oldest one as the edit adding the reference. We evaluated the proposed method by using the data set based on DOI links referenced on English Wikipedia. As a result, the accuracy was 93.3% as a whole and over 90% in 20 out of 22 research fields. We showed that the proposed method was able to identify the edits adding bibliographic references at a high accuracy regardless of research fields.

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Big Scholarly Data in CiteSeerX

Cited by 8 publications

References 30 publications

A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents

A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents

A survey on scholarly data: From big data perspective

A Method to Identify the Edits Adding Bibliographic References to Wikipedia

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