ArenaRune/scitldr-t5-base-lora
Summarization • Updated
• 34
Datasets:
source sequence | source_labels sequence | rouge_scores sequence | paper_id stringlengths 9 11 | ic unknown | target sequence |
|---|---|---|---|---|---|
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"We provide necessary and sufficient analytical forms for the critical points of the square loss functions for various neural networks, and exploit the analytical forms to characterize the landscape properties for the loss functions of these neural networks."
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"The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain.",
"One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways.",
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"Biologically plausible learning algorithms, particularly sign-symmetry, work well on ImageNet"
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"We introduce the 2-simplicial Transformer and show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning."
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"We present Tensor-Train RNN (TT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics.",
"Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity ... | [
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"Accurate forecasting over very long time horizons using tensor-train RNNs"
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"Modern deep neural networks have a large amount of weights, which make them difficult to deploy on computation constrained devices such as mobile phones.",
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"A simple modification to low-rank factorization that improves performances (in both image and language tasks) while still being compact."
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"Deep learning training accesses vast amounts of data at high velocity, posing challenges for datasets retrieved over commodity networks and storage devices.",
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"We propose a simple, general, and space-efficient data format to accelerate deep learning training by allowing sample fidelity to be dynamically selected at training time"
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"It is fundamental and challenging to train robust and accurate Deep Neural Networks (DNNs) when semantically abnormal examples exist.",
"Although great progress has been made, there is still one crucial research question which is not thoroughly explored yet: What training examples should be focused and how much ... | [
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"ROBUST DISCRIMINATIVE REPRESENTATION LEARNING VIA GRADIENT RESCALING: AN EMPHASIS REGULARISATION PERSPECTIVE"
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"Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images.",
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End of preview. Expand
in Data Studio
Dataset Card for SciTLDR
Dataset Summary
SciTLDR: Extreme Summarization of Scientific Documents
SciTLDR is a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden.
Supported Tasks and Leaderboards
summarization
Languages
English
Dataset Structure
SciTLDR is split in to a 60/20/20 train/dev/test split. For each file, each line is a json, formatted as follows
{
"source":[
"sent0",
"sent1",
"sent2",
...
],
"source_labels":[binary list in which 1 is the oracle sentence],
"rouge_scores":[precomputed rouge-1 scores],
"paper_id":"PAPER-ID",
"target":[
"author-tldr",
"pr-tldr0",
"pr-tldr1",
...
],
"title":"TITLE"
}
The keys rouge_scores and source_labels are not necessary for any code to run, precomputed Rouge scores are provided for future research.
Data Instances
{ "source": [ "Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs.", "MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training.", "Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages.", "We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter.", "We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods.", "We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point." ], "source_labels": [ 0, 0, 0, 1, 0, 0 ], "rouge_scores": [ 0.2399999958000001, 0.26086956082230633, 0.19999999531250012, 0.38095237636054424, 0.2051282003944774, 0.2978723360796741 ], "paper_id": "rJlnfaNYvB", "target": [ "We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.", "Proposal for an adaptive loss scaling method during backpropagation for mix precision training where scale rate is decided automatically to reduce the underflow.", "The authors propose a method to train models in FP16 precision that adopts a more elaborate way to minimize underflow in every layer simultaneously and automatically." ], "title": "Adaptive Loss Scaling for Mixed Precision Training" }
Data Fields
source: The Abstract, Introduction and Conclusion (AIC) or Full text of the paper, with one sentence per line.source_labels: Binary 0 or 1, 1 denotes the oracle sentence.rouge_scores: Precomputed ROUGE baseline scores for each sentence.paper_id: Arxiv Paper ID.target: Multiple summaries for each sentence, one sentence per line.title: Title of the paper.
Data Splits
| train | valid | test | |
|---|---|---|---|
| SciTLDR-A | 1992 | 618 | 619 |
| SciTLDR-AIC | 1992 | 618 | 619 |
| SciTLDR-FullText | 1992 | 618 | 619 |
Dataset Creation
[More Information Needed]
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
Annotations
Annotation process
Given the title and first 128 words of a reviewer comment about a paper, re-write the summary (if it exists) into a single sentence or an incomplete phrase. Summaries must be no more than one sentence. Most summaries are between 15 and 25 words. The average rewritten summary is 20 words long.
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
To encourage further research in the area of extreme summarization of scientific documents.
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
Apache License 2.0
Citation Information
@article{cachola2020tldr, title={{TLDR}: Extreme Summarization of Scientific Documents}, author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld}, journal={arXiv:2004.15011}, year={2020}, }
Contributions
Thanks to @Bharat123rox for adding this dataset.
- Downloads last month
- 881
Homepage:
github.com
Repository:
github.com
Paper:
arxiv.org
Point of Contact:
{isabelc,kylel,armanc,danw}@allenai.org
Size of downloaded dataset files:
142 MB
Size of the auto-converted Parquet files:
66.8 MB
Number of rows:
9,687
