Update README.md

README.md

@@ -1,41 +1,96 @@
----
-dataset_info:
-- config_name: default
-  features:
-  - name: condition_label
-    dtype: int64
-  - name: medical_abstract
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 14334753
-    num_examples: 11550
-  - name: test
-    num_bytes: 3606846
-    num_examples: 2888
-  download_size: 9606491
-  dataset_size: 17941599
-- config_name: labels
-  features:
-  - name: condition_label
-    dtype: int64
-  - name: condition_name
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 171
-    num_examples: 5
-  download_size: 1611
-  dataset_size: 171
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
-- config_name: labels
-  data_files:
-  - split: train
-    path: labels/train-*
----
+---
+dataset_info:
+- config_name: default
+  features:
+  - name: condition_label
+    dtype: int64
+  - name: medical_abstract
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 14334753
+    num_examples: 11550
+  - name: test
+    num_bytes: 3606846
+    num_examples: 2888
+  download_size: 9606491
+  dataset_size: 17941599
+- config_name: labels
+  features:
+  - name: condition_label
+    dtype: int64
+  - name: condition_name
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 171
+    num_examples: 5
+  download_size: 1611
+  dataset_size: 171
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
+- config_name: labels
+  data_files:
+  - split: train
+    path: labels/train-*
+license: cc-by-sa-3.0
+task_categories:
+- text-classification
+language:
+- en
+pretty_name: Medical Abstracts Text Classification Dataset
+size_categories:
+- 10K<n<100K
+---
+
+# Medical-Abstracts-TC-Corpus
+This repository contains a medical abstracts dataset, describing 5 different classes of patient conditions. The dataset can be used for text classification. 
+
+
+📄 Paper: [Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches (NLPIR 2022)](https://doi.org/10.1145/3582768.3582795).
+
+💻 GitHub: [https://github.com/sebischair/Medical-Abstracts-TC-Corpus](https://github.com/sebischair/Medical-Abstracts-TC-Corpus)
+
+Summary of the medical abstracts dataset:
+
+* The `default` subset contains the train and test splits with numerical class labels.
+* The `labels` subset contains the textual names of the numerical class labels.
+
+| **Class name**                  | **#training** | **#test** | **Total** |
+|---------------------------------|---------------|-----------|-----------|
+| Neoplasms                       | 2530          | 633       | 3163      |
+| Digestive system diseases       | 1195          | 299       | 1494      |
+| Nervous system diseases         | 1540          | 385       | 1925      |
+| Cardiovascular diseases         | 2441          | 610       | 3051      |
+| General pathological conditions | 3844          | 961       | 4805      |
+| **Total**                       | **11550**     | **2888**  | **14438** |
+
+## Citation information
+
+This dataset was created during the writing of our paper titled [Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches](https://doi.org/10.1145/3582768.3582795).
+
+When citing this medical abstracts dataset in academic papers and theses, please use the following BibTeX entry:
+``` 
+@inproceedings{10.1145/3582768.3582795,
+author = {Schopf, Tim and Braun, Daniel and Matthes, Florian},
+title = {Evaluating Unsupervised Text Classification: Zero-Shot and Similarity-Based Approaches},
+year = {2023},
+isbn = {9781450397629},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3582768.3582795},
+doi = {10.1145/3582768.3582795},
+abstract = {Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to generalize knowledge gained from a training task by assigning appropriate labels of unknown classes to text documents. Although existing studies have already investigated individual approaches to these categories, the experiments in literature do not provide a consistent comparison. This paper addresses this gap by conducting a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes. Different state-of-the-art approaches are benchmarked on four text classification datasets, including a new dataset from the medical domain. Additionally, novel SimCSE [7] and SBERT-based [26] baselines are proposed, as other baselines used in existing work yield weak classification results and are easily outperformed. Finally, the novel similarity-based Lbl2TransformerVec approach is presented, which outperforms previous state-of-the-art approaches in unsupervised text classification. Our experiments show that similarity-based approaches significantly outperform zero-shot approaches in most cases. Additionally, using SimCSE or SBERT embeddings instead of simpler text representations increases similarity-based classification results even further.},
+booktitle = {Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval},
+pages = {6–15},
+numpages = {10},
+keywords = {Zero-shot Text Classification, Natural Language Processing, Unsupervised Text Classification},
+location = {Bangkok, Thailand},
+series = {NLPIR '22}
+}
+```