language: en
license: mit
tags:
- keyphrase-generation
datasets:
- midas/openkp
widget:
- text: >-
Keyphrase extraction is a technique in text analysis where you extract the
important keyphrases from a text. Since this is a time-consuming process,
Artificial Intelligence is used to automate it. Currently, classical
machine learning methods, that use statistics and linguistics, are widely
used for the extraction process. The fact that these methods have been
widely used in the community has the advantage that there are many
easy-to-use libraries. Now with the recent innovations in deep learning
methods (such as recurrent neural networks and transformers, GANS, …),
keyphrase extraction can be improved. These new methods also focus on the
semantics and context of a document, which is quite an improvement. Thanks
to the introduction of neural networks, it's also possible to generate
related keyphrases based on a given text document. This is useful, for
example, when an author wants to make his work findable.
example_title: Example 1
- text: >-
In this work, we explore how to learn task specific language models aimed
towards learning rich representation of keyphrases from text documents. We
experiment with different masking strategies for pre-training transformer
language models (LMs) in discriminative as well as generative settings. In
the discriminative setting, we introduce a new pre-training objective -
Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains
in performance (up to 9.26 points in F1) over SOTA, when LM pre-trained
using KBIR is fine-tuned for the task of keyphrase extraction. In the
generative setting, we introduce a new pre-training setup for BART -
KeyBART, that reproduces the keyphrases related to the input text in the
CatSeq format, instead of the denoised original input. This also led to
gains in performance (up to 4.33 points inF1@M) over SOTA for keyphrase
generation. Additionally, we also fine-tune the pre-trained language
models on named entity recognition(NER), question answering (QA), relation
extraction (RE), abstractive summarization and achieve comparable
performance with that of the SOTA, showing that learning rich
representation of keyphrases is indeed beneficial for many other
fundamental NLP tasks.
example_title: Example 2
model-index:
- name: DeDeckerThomas/keyphrase-generation-t5-small-openkp
results:
- task:
type: keyphrase-generation
name: Keyphrase Generation
dataset:
type: midas/openkp
name: openkp
metrics:
- type: F1@M (Present)
value: 0
name: F1@M (Present)
- type: F1@O (Present)
value: 0
name: F1@O (Present)
- type: F1@M (Absent)
value: 0
name: F1@M (Absent)
- type: F1@O (Absent)
value: 0
name: F1@O (Absent)
🔑 Keyphrase Generation model: T5-small-OpenKP
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text. Since this is a time-consuming process, Artificial Intelligence is used to automate it. Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries. Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved. These new methods also focus on the semantics and context of a document, which is quite an improvement. Thanks to the introduction of neural networks, it's also possible to generate related keyphrases based on a given text document. This is useful, for example, when an author wants to make his work findable.
📓 Model Description
This model is a fine-tuned T5-small model on the OpenKP dataset.
✋ Intended uses & limitations
🛑 Limitations
- Only works for English documents.
- For a custom model, please consult the training notebook for more information (link incoming).
- Sometimes the output doesn't make any sense.
❓ How to use
# Model parameters
from transformers import (
Text2TextGenerationPipeline,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
import numpy as np
class KeyphraseGenerationPipeline(Text2TextGenerationPipeline):
def __init__(self, model, keyphrase_sep_token=";", *args, **kwargs):
super().__init__(
model=AutoModelForSeq2SeqLM.from_pretrained(model),
tokenizer=AutoTokenizer.from_pretrained(model),
*args,
**kwargs
)
self.keyphrase_sep_token = keyphrase_sep_token
def postprocess(self, model_outputs):
results = super().postprocess(
model_outputs=model_outputs
)
return [[keyphrase.strip() for keyphrase in result.get("generated_text").split(self.keyphrase_sep_token)] for result in results]
# Load pipeline
model_name = "DeDeckerThomas/keyphrase-generation-t5-small-openkp"
generator = KeyphraseGenerationPipeline(model=model_name)
```python
text = """
Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a text.
Since this is a time-consuming process, Artificial Intelligence is used to automate it.
Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process.
The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries.
Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved.
These new methods also focus on the semantics and context of a document, which is quite an improvement.
""".replace(
"\n", ""
)
keyphrases = generator(text)
print(keyphrases)
# Output
[['keyphrase extraction', 'text analysis', 'artificial intelligence', 'classical machine learning', 'statistics']]
📚 Training Dataset
OpenKP is a large-scale, open-domain keyphrase extraction dataset with 148,124 real-world web documents along with 1-3 most relevant human-annotated keyphrases.
You can find more information here: https://github.com/microsoft/OpenKP.
👷♂️ Training procedure
For more in detail information, you can take a look at the training notebook (link incoming).
Training parameters
| Parameter | Value |
|---|---|
| Learning Rate | 5e-5 |
| Epochs | 50 |
| Early Stopping Patience | 1 |
Preprocessing
Postprocessing
📝 Evaluation results
One of the traditional evaluation methods is the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases. The model achieves the following results on the OpenKP test set:
Extractive keyphrases
| Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M | P@O | R@O | F1@O |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OpenKP Test Set | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Abstractive keyphrases
| Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M | P@O | R@O | F1@O |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OpenKP Test Set | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
For more information on the evaluation process, you can take a look at the keyphrase extraction evaluation notebook.
🚨 Issues
Please feel free to contact Thomas De Decker for any problems with this model.