| Challenge: | Citation recommendation tasks involve recommending citations within their specific contexts. |
| Approach: | They propose to use arXiv.org's citation-dependent evaluation data set to evaluate citations . their data set is characterized by the fact that it exhibits almost zero noise in its extracted content . |
| Outcome: | The proposed data set exhibits almost zero noise in extracted content and all citations are linked to their correct publications. |
Similar Papers
Towards a Gold Standard Corpus for Variable Detection and Linking in Social Science Publications (L18-1)
Copied to clipboard
| Challenge: | a new corpus for detecting and linking survey variables is being developed . the corpus is multilingual and includes manually curated word and phrase alignments . |
| Approach: | They propose to create a corpus for the evaluation of detecting and linking survey variables in social science publications. |
| Outcome: | The proposed corpus is the first gold standard for the variable detection and linking task. |
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery (2020.lrec-1)
Copied to clipboard
| Challenge: | Recent studies have proposed to take advantage of the scientific paper's citation network to approach literature summarization. |
| Approach: | They propose to annotate related work sections, cite papers and sentences using machine readable data and an additional layer of papers citing the references. |
| Outcome: | The proposed corpus expands the existing data-set of related work sections and cites the papers cited in the related work section. |
CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision (2022.emnlp-main)
Copied to clipboard
| Challenge: | Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers . previous attempts failed to scale up due to heavy human annotation and domain expertise . |
| Approach: | They propose a method to automatically extract TLDR summaries from scientific papers . they propose 'citeSum' with no human annotation, which is 30 times larger than SciTLDR . |
| Outcome: | The proposed approach outperforms most fully-supervised methods on SciTLDR without fine-tuning and achieves state-of-the-art results with only 128 examples. |
TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent efforts to extract tasks, datasets and evaluation metrics from scientific literature focus on abstracts only. |
| Approach: | They propose a corpus that contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities extracted from NLP papers. |
| Outcome: | The proposed corpus contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities extracted from NLP papers. |
A Fair and In-Depth Evaluation of Existing End-to-End Entity Linking Systems (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing evaluations of entity linking systems often lack detailed error analysis or a closer look at the results. |
| Approach: | They evaluate existing entity linking systems and propose two new benchmarks . they characterize their strengths and weaknesses and report on reproducibility aspects . |
| Outcome: | The evaluations of existing system have strong biases and artifacts . they characterize their strengths and weaknesses and report on reproducibility aspects . |
Boosting Entity Linking Performance by Leveraging Unlabeled Documents (P19-1)
Copied to clipboard
| Challenge: | a new approach to entity linking relies on unlabeled documents and Wikipedia . a supervised approach uses only natural information, such as unlabed documents . |
| Approach: | They propose a method which exploits only naturally occurring information . they construct a high recall list of candidate entities for each mention in an unlabeled document . |
| Outcome: | The proposed model outperforms fully-supervised state-of-the-art systems on standard test sets. |
Scientific Statement Classification over arXiv.org (2020.lrec-1)
Copied to clipboard
| Challenge: | a dataset of 1.2 million documents converted from the original submissions is available for supervised learning. |
| Approach: | They propose a new classification task for scientific statements and a large-scale dataset for supervised learning. |
| Outcome: | The proposed task achieves a 0.91 F1 score and a lexeme serialization for mathematical formulas. |
Dataset Construction for Scientific-Document Writing Support by Extracting Related Work Section and Citations from PDF Papers (2022.lrec-1)
Copied to clipboard
| Challenge: | To augment datasets used for scientific-document writing support research, we extract texts from “Related Work” sections and citation information in PDF-formatted papers published in English. |
| Approach: | They propose to extract text from “Related Work” sections and citation information from PDF-formatted papers published in English. |
| Outcome: | The proposed dataset is based on a previously constructed dataset using only Tex papers and is compared with the existing one. |
The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources (2020.lrec-1)
Copied to clipboard
| Challenge: | a dataset for scientific entity extraction, classification, and resolution has been developed . a generic conceptual formalism for scientific entities is feasible, the authors say . |
| Approach: | They propose a STEM-ECR dataset that provides a domain-independent benchmark for scientific entity extraction, classification, and resolution tasks. |
| Outcome: | The proposed dataset provides a benchmark for evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. |
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing work on scientific information extraction (SciIE) considers extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature. |
| Approach: | They propose to automate the extraction of key information from scientific documents by leveraging a complementary source: the citation graph of referential links between citing and cited papers. |
| Outcome: | The proposed model improves on a set of English-language scientific documents. |