Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models

• URL: http://arxiv.org/abs/2405.12206v1
• Date: Mon, 20 May 2024 17:45:36 GMT
• Title: Modeling citation worthiness by using attention-based bidirectional long short-term memory networks and interpretable models
• Authors: Tong Zeng, Daniel E. Acuna,
• Abstract summary: We propose a Bidirectional Long Short-Term Memory (BiLSTM) network with attention mechanism and contextual information to detect sentences that need citations. We produce a new, large dataset (PMOA-CITE) based on PubMed Open Access Subset, which is orders of magnitude larger than previous datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Scientist learn early on how to cite scientific sources to support their claims. Sometimes, however, scientists have challenges determining where a citation should be situated -- or, even worse, fail to cite a source altogether. Automatically detecting sentences that need a citation (i.e., citation worthiness) could solve both of these issues, leading to more robust and well-constructed scientific arguments. Previous researchers have applied machine learning to this task but have used small datasets and models that do not take advantage of recent algorithmic developments such as attention mechanisms in deep learning. We hypothesize that we can develop significantly accurate deep learning architectures that learn from large supervised datasets constructed from open access publications. In this work, we propose a Bidirectional Long Short-Term Memory (BiLSTM) network with attention mechanism and contextual information to detect sentences that need citations. We also produce a new, large dataset (PMOA-CITE) based on PubMed Open Access Subset, which is orders of magnitude larger than previous datasets. Our experiments show that our architecture achieves state of the art performance on the standard ACL-ARC dataset ($F_{1}=0.507$) and exhibits high performance ($F_{1}=0.856$) on the new PMOA-CITE. Moreover, we show that it can transfer learning across these datasets. We further use interpretable models to illuminate how specific language is used to promote and inhibit citations. We discover that sections and surrounding sentences are crucial for our improved predictions. We further examined purported mispredictions of the model, and uncovered systematic human mistakes in citation behavior and source data. This opens the door for our model to check documents during pre-submission and pre-archival procedures. We make this new dataset, the code, and a web-based tool available to the community.

Related papers

• WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations [34.99831757956635]
  We formulate the task of attributed query-focused summarization (AQFS) and present WebCiteS, a Chinese dataset featuring 7k human-annotated summaries with citations. We tackle these issues by developing detailed metrics and enabling the automatic evaluator to decompose the sentences into sub-claims for fine-grained verification.
  arXiv  Detail & Related papers  (2024-03-04T07:06:41Z)
• Interactive Distillation of Large Single-Topic Corpora of Scientific Papers [1.2954493726326113]
  A more robust but time-consuming approach is to build the dataset constructively in which a subject matter expert handpicks documents. Here we showcase a new tool, based on machine learning, for constructively generating targeted datasets of scientific literature.
  arXiv  Detail & Related papers  (2023-09-19T17:18:36Z)
• Enabling Large Language Models to Generate Text with Citations [37.64884969997378]
  Large language models (LLMs) have emerged as a widely-used tool for information seeking. Our aim is to allow LLMs to generate text with citations, improving their factual correctness and verifiability. We propose ALCE, the first benchmark for Automatic LLMs' Citation Evaluation.
  arXiv  Detail & Related papers  (2023-05-24T01:53:49Z)
• BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction [13.361489059744754]
  BLIAM generates training data points that are interpretable and model-agnostic to downstream applications. BLIAM can be further used to synthesize data points for novel drugs and cell lines that were not even measured in biomedical experiments.
  arXiv  Detail & Related papers  (2023-02-14T06:48:52Z)
• The Semantic Scholar Open Data Platform [79.4493235243312]
  Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature. We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction. The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings.
  arXiv  Detail & Related papers  (2023-01-24T17:13:08Z)
• Scientific Paper Extractive Summarization Enhanced by Citation Graphs [50.19266650000948]
  We focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings. Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework. Motivated by this, we propose a Graph-based Supervised Summarization model (GSS) to achieve more accurate results on the task when large-scale labeled data are available.
  arXiv  Detail & Related papers  (2022-12-08T11:53:12Z)
• Autoregressive Search Engines: Generating Substrings as Document Identifiers [53.0729058170278]
  Autoregressive language models are emerging as the de-facto standard for generating answers. Previous work has explored ways to partition the search space into hierarchical structures. In this work we propose an alternative that doesn't force any structure in the search space: using all ngrams in a passage as its possible identifiers.
  arXiv  Detail & Related papers  (2022-04-22T10:45:01Z)
• Towards generating citation sentences for multiple references with intent control [86.53829532976303]
  We build a novel generation model with the Fusion-in-Decoder approach to cope with multiple long inputs. Experiments demonstrate that the proposed approaches provide much more comprehensive features for generating citation sentences.
  arXiv  Detail & Related papers  (2021-12-02T15:32:24Z)
• CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding [23.930041685595775]
  We present an in-depth study of cite-worthiness detection in English, where a sentence is labelled for whether or not it cites an external source. CiteWorth is high-quality, challenging, and suitable for studying problems such as domain adaptation.
  arXiv  Detail & Related papers  (2021-05-23T11:08:45Z)
• Hidden Biases in Unreliable News Detection Datasets [60.71991809782698]
  We show that selection bias during data collection leads to undesired artifacts in the datasets. We observed a significant drop (>10%) in accuracy for all models tested in a clean split with no train/test source overlap. We suggest future dataset creation include a simple model as a difficulty/bias probe and future model development use a clean non-overlapping site and date split.
  arXiv  Detail & Related papers  (2021-04-20T17:16:41Z)
• ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning [85.33459673197149]
  We introduce a new Reading dataset requiring logical reasoning (ReClor) extracted from standardized graduate admission examinations. In this paper, we propose to identify biased data points and separate them into EASY set and the rest as HARD set. Empirical results show that state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set. However, they struggle on HARD set with poor performance near that of random guess, indicating more research is needed to essentially enhance the logical reasoning ability of current models.
  arXiv  Detail & Related papers  (2020-02-11T11:54:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.