On the behalf of our team, I’m pleased to announce the release of SciSumm14, an annotated corpus for scientific summarization.
CL-SciSumm 2017 is an open repository with a corpus of ACL Computational Linguistics research papers and their annotations, contributed to the public by the Web IR / NLP Group at the National University of Singapore (WING-NUS). This corpus is offered as a part of the SciSumm Shared Task.
This corpus is expected to be of interest to a broad community including those working in computational linguistics NLP, text summarization, discourse structure in scholarly discourse, paraphrase, textual entailment, and/or text simplification.
WEBSITE AND COMPLETE CALL:
Dr. Kokil Jaidka (alumnus, Wee Kim Wee School of Communication and Information, Nanyang Technological University)
Dr. Min-Yen Kan (Dept. of Computer Science, School of Computing, National University of Singapore)
Muthu Kumar Chandrasekaran (Dept. of Computer Science, School of Computing, National University of Singapore)
SUMMARY OF CORPUS PROPERTIES:
1. Created by randomly sampling ten documents from the ACL Anthology corpus and selecting their citing papers. It is available for download at https://github.com/WING-NUS/scisumm-corpus
2. Organized into “topic” folders. Each “topic” is the Reference Paper, and the folder contains ten or more Citing Papers (CPs) that all contain citations to the RP. In each CP, the text spans (i.e., citances) have been identified that pertain to a particular citation to the RP.
3. Most text files were created from the pdf files obtained above by using Adobe Acrobat. The remaining were converted using the GATE 8.0 open source software. For more details, see the README at https://github.com/WING-NUS/scisumm-corpus
4. Inter-annotator agreement was used to assess the homogeneity and quality of the coding of citances and references, and disagreements were resolved through discussion.
5. The ACL ids and the titles of selected reference papers (out of 50 total) are given below:
ACL-anthology-id Tile of the paper
H89-2014 Augmenting a Hidden Markov Model for Phrase-Dependent Word Tagging
X96-1048 OVERVIEW OF RESULTS OF THE MUC-6 EVALUATION
C94-2154 The correct and efficient implementation of appropriate specifications for typed feature structures
E03-1020 Discovering Corpus-Specific Word Senses
C90-2039 Strategic Lazy Incremental Copy Graph Unification
J00-3003 Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
P98-1081 Improving Data Driven Wordclass Tagging by System Combination
N01-1011 A Decision Tree of Bigrams is an Accurate Predictor of Word Sense
H05-1115 Using Random Walks for Question-focused Sentence Retrieval
J98-2005 Estimation of Probabilistic Context-Free Grammars