Korea, Republic of

Recently, there have been numerous research in Natural Language Processing on citation analysis in scientific literature. Studies of citation behavior aim at finding how researchers cited a paper in their work.
In this paper, we are interested in identifying cited papers that are criticized. 
Recent research introduces the concept of Critical citations which provides a useful theoretical framework, making criticism an important part of scientific progress. Indeed, identifying critics could be a way to spot errors and thus encourage self-correction of science.
In this work, we investigate how to automatically classify the critical citation contexts using Natural Language Processing (NLP). Our classification task consists of predicting critical or non-critical labels for citation contexts. 
For this, we experiment and compare different methods, including rule-based and machine learning methods, to classify critical vs. non-critical citation contexts.
Our experiments show that fine-tuning pretrained transformer model RoBERTa achieved the highest performance among all systems.

COLING 2022

Citation Context Classification: Critical vs Non-critical

scientific papers

criticism

rules

citation

machine learning

classification

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Communicative functions are an important rhetorical feature of scientific writing. Sentence embeddings that contain such features are highly valuable for the argumentative analysis of scientific documents, with applications in document alignment, recommendation, and academic writing assistance. Moreover, embeddings can provide a possible solution to the open-set problem, where models need to generalize to new communicative functions unseen at training time. However, existing sentence representation models are not suited for detecting functional similarity since they only consider lexical or semantic similarities. To remedy this, we propose a combined approach of distant supervision and metric learning to make a representation model more aware of the functional part of a sentence. We first leverage an existing academic phrase database to label sentences automatically with their functions. Then, we train an embedding model to capture similarities and dissimilarities from a rhetorical perspective. The experimental results demonstrate that the embeddings obtained from our model are more advantageous than existing models when retrieving functionally similar sentences. We also provide an extensive analysis of the performance differences between five metric learning objectives, revealing that traditional methods (e.g., softmax cross-entropy loss and triplet loss) outperform state-of-the-art techniques.

Incorporating the Rhetoric of Scientific Language into Sentence Embeddings using Phrase-guided Distant Supervision and Metric Learning

Existing dense retrieval models for scientific documents have been optimized for either retrieval by short queries, or for document similarity, but usually not for both. In this paper we explore the space of combining multiple objectives to achieve a single representation model that presents a good balance between both modes of dense retrieval, combining the relevance judgements from MS MARCO with the citation similarity of SPECTER, and the self-supervised objective of independent cropping. We also consider the addition of training data from document co-citation in a sentence context and domain-specific synthetic data. We show that combining multiple objectives yields models that generalize well across different benchmark tasks, improving up to 73\% over models trained on a single objective.

Multi-objective Representation Learning for Scientific Document Retrieval

The meaning and usage of a concept or a word changes over time. These diachronic semantic shifts reflect the change of societal and cultural consensus as well as the evolution of science. The availability of large-scale corpora and recent success in language models have enabled researchers to analyze semantic shifts in great detail. However, current research lacks intuitive ways of presenting diachronic semantic shifts and making them comprehensive. In this paper, we study the PubMed dataset and compute semantic shifts across six decades. We develop three visualization methods that can show, given a root word: the temporal change in its linguistic 
context, word re-occurrence, degree of similarity, time continuity, and separate
trends per geographic location. We also propose a taxonomy that classifies visualization methods for diachronic semantic shifts with respect to different purposes.

Visualisation Methods for Diachronic Semantic Shift

Given a citation in the body of a research paper, cited text identification aims to find the sentences in the cited paper that are most relevant to the citing sentence. The task is fundamentally one of sentence matching, where affinity is often assessed by a cosine similarity between sentence embeddings. However, (a) sentences may not be well-represented by a single embedding because they contain multiple distinct semantic aspects, and (b) good matches may not require a strong match in all aspects. To overcome these limitations, we propose a simple and efficient unsupervised method for cited text identification that adapts an asymmetric similarity measure to allow partial matches of multiple aspects in both sentences. On the CL-SciSumm dataset we find that our method outperforms a baseline symmetric approach, and, surprisingly, also outperforms all supervised and unsupervised systems submitted to past editions of CL-SciSumm Shared Task 1a.

Unsupervised Partial Sentence Matching for Cited Text Identification

Automatic organization of scholarly literature is a challenging but essential task. In particular, assigning key concepts to scientific publications allows researchers, policymakers, and the general public to search for and discover relevant research. But any meaningful organization of scientific publications must evolve with new research, requiring up-to-date and scalable text classification models. Additionally, scientific research publications benefit from multi-label classification, particularly with more fine-grained sub-domains. Prior work has focused on classifying scientific publications from one research area (e.g., computer science), referencing static concept descriptions, and implementing English-only classification models. We propose a multi-label classification model that can be implemented in non-English languages, across all scientific literature, with dynamic concepts.

Multi-label Classification of Scientific Research Documents Across Domains and Languages

Long document summarization, a challenging summarization scenario, is the focus of the recently proposed LongSumm shared task. One of the limitations of this shared task has been its use of a single family of metrics for evaluation (the ROUGE metrics). In contrast, other fields, like text generation, employ multiple metrics. We replicated the LongSumm evaluation using multiple test set samples (vs. the single test set of the official shared task) and investigated how different metrics might complement each other in this evaluation framework. We show that under this more rigorous evaluation, (1) some of the key learnings from Longsumm 2020 and 2021 still hold, but the relative ranking of systems changes, and (2) the use of additional metrics reveals additional high-quality summaries missed by ROUGE, and (3) we show that SPICE is a candidate metric for summarization evaluation for LongSumm.

Investigating Metric Diversity for Evaluating Long Document Summarisation

Relation extraction models typically cast the problem of determining whether there is a relation between a pair of entities as a single decision. However, these models can struggle with long or complex language constructions in which two entities are not directly linked, as is often the case in scientific publications. We propose a novel approach that decomposes a binary relation into two unary relations that capture each argument's role in the relation separately. We create a stacked learning model that incorporates information from unary and binary relation extractors to determine whether a relation holds between two entities. We present experimental results showing that this approach outperforms several competitive relation extractors on a new corpus of planetary science publications as well as a benchmark dataset in the biology domain.

Exploiting Unary Relations with Stacked Learning for Relation Extraction

We release a pretrained Japanese masked lan-
guage model for an academic domain. Pre-
trained masked language models have recently
improved the performance of various natural
language processing applications. In domains
such as medical and academic, which include
a lot of technical terms, domain-specific pre-
training is effective. While domain-specific
masked language models for medical and SNS
domains are widely used in Japanese, along
with domain-independent ones, pretrained mod-
els specific to the academic domain are not
publicly available. In this study, we pretrained
a RoBERTa-based Japanese masked language
model on paper abstracts from the academic
database CiNii Articles. Experimental results
on Japanese text classification in the academic
domain revealed the effectiveness of the pro-
posed model over existing pretrained models.

A Japanese Masked Language Model for Academic Domain

[Mask]ing named entities during pretraining helps BART to preserve named entities in a generated summary:
We address the named entity omission - the drawback of many current abstractive text summarizers. We suggest a custom pretraining objective to enhance the model’s attention on the named entities in a text.

Named Entity Inclusion in Abstractive Text Summarization

A scientific paper is traditionally prefaced by an abstract that summarizes the paper. Recently, research highlights that focus on the main findings of the paper have emerged as a complementary summary in addition to an abstract. However, highlights are not yet as common as abstracts, and are absent in many papers.
In this paper, we aim to automatically generate research highlights using different sections of a research paper as input. We investigate whether the use of named entity recognition on the input improves the quality of the generated highlights. In particular, we have used two deep learning-based models: the first is a pointer-generator network, and the second augments the first model with coverage mechanism. We then augment each of the above models with named entity recognition features. 
The proposed method can be used to produce highlights for papers with missing highlights. Our experiments show that adding named entity information improves the performance of the deep learning-based summarizers in terms of ROUGE, METEOR and BERTScore measures.

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Citation Context Classification: Critical vs Non-critical

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Incorporating the Rhetoric of Scientific Language into Sentence Embeddings using Phrase-guided Distant Supervision and Metric Learning

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