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+ # Gupshup
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+ GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
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+ Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
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+ Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
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+
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+ ### Dataset
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+ Please request for the Gupshup data using [this Google form](https://docs.google.com/forms/d/1zvUk7WcldVF3RCoHdWzQPzPprtSJClrnHoIOYbzaJEI/edit?ts=61381ec0).
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+
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+ Dataset is available for `Hinglish Dilaogues to English Summarization`(h2e) and `English Dialogues to English Summarization`(e2e). For each task, Dialogues/conversastion have `.source`(train.source) as file extension whereas Summary has `.target`(train.target) file extension. ".source" file need to be provided to `input_path` and ".target" file to `reference_path` argument in the scripts.
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+
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+
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+ ## Models
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+ All model weights are available on the Huggingface model hub. Users can either directly download these weights in their local and provide this path to `model_name` argument in the scripts or use the provided alias (to `model_name` argument) in scripts directly; this will lead to download weights automatically by scripts.
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+
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+ Model names were aliased in "gupshup_TASK_MODEL" sense, where "TASK" can be h2e,e2e and MODEL can be mbart, pegasus, etc., as listed below.
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+
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+ **1. Hinglish Dialogues to English Summary (h2e)**
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+
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+ | Model | Huggingface Alias |
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+ |---------|-------------------------------------------------------------------------------|
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+ | mBART | [midas/gupshup_h2e_mbart](https://huggingface.co/midas/gupshup_h2e_mbart) |
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+ | PEGASUS | [midas/gupshup_h2e_pegasus](https://huggingface.co/midas/gupshup_h2e_pegasus) |
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+ | T5 MTL | [midas/gupshup_h2e_t5_mtl](https://huggingface.co/midas/gupshup_h2e_t5_mtl) |
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+ | T5 | [midas/gupshup_h2e_t5](https://huggingface.co/midas/gupshup_h2e_t5) |
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+ | BART | [midas/gupshup_h2e_bart](https://huggingface.co/midas/gupshup_h2e_bart) |
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+ | GPT-2 | [midas/gupshup_h2e_gpt](https://huggingface.co/midas/gupshup_h2e_gpt) |
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+
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+
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+ **2. English Dialogues to English Summary (e2e)**
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+
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+ | Model | Huggingface Alias |
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+ |---------|-------------------------------------------------------------------------------|
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+ | mBART | [midas/gupshup_e2e_mbart](https://huggingface.co/midas/gupshup_e2e_mbart) |
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+ | PEGASUS | [midas/gupshup_e2e_pegasus](https://huggingface.co/midas/gupshup_e2e_pegasus) |
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+ | T5 MTL | [midas/gupshup_e2e_t5_mtl](https://huggingface.co/midas/gupshup_e2e_t5_mtl) |
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+ | T5 | [midas/gupshup_e2e_t5](https://huggingface.co/midas/gupshup_e2e_t5) |
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+ | BART | [midas/gupshup_e2e_bart](https://huggingface.co/midas/gupshup_e2e_bart) |
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+ | GPT-2 | [midas/gupshup_e2e_gpt](https://huggingface.co/midas/gupshup_e2e_gpt) |
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+
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+ ## Inference
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+
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+ ### Using command line
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+ 1. Clone this repo and create a python virtual environment (https://docs.python.org/3/library/venv.html). Install the required packages using
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+ ```
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+ git clone https://github.com/midas-research/gupshup.git
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+ pip install -r requirements.txt
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+ ```
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+
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+ 2. run_eval script has the following arguments.
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+ * **model_name** : Path or alias to one of our models available on Huggingface as listed above.
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+ * **input_path** : Source file or path to file containing conversations, which will be summarized.
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+ * **save_path** : File path where to save summaries generated by the model.
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+ * **reference_path** : Target file or path to file containing summaries, used to calculate matrices.
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+ * **score_path** : File path where to save scores.
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+ * **bs** : Batch size
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+ * **device**: Cuda devices to use.
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+
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+ Please make sure you have downloaded the Gupshup dataset using the above google form and provide the correct path to these files in the argument's `input_path` and `refrence_path.` Or you can simply put `test.source` and `test.target` in `data/h2e/`(hinglish to english) or `data/e2e/`(english to english) folder. For example, to generate English summaries from Hinglish dialogues using the mbart model, run the following command
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+
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+ ```
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+ python run_eval.py \
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+ --model_name midas/gupshup_h2e_mbart \
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+ --input_path data/h2e/test.source \
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+ --save_path generated_summary.txt \
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+ --reference_path data/h2e/test.target \
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+ --score_path scores.txt \
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+ --bs 8
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+
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+ ```
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+
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+ Another example, to generate English summaries from English dialogues using the Pegasus model
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+ ```
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+ python run_eval.py \
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+ --model_name midas/gupshup_e2e_pegasus \
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+ --input_path data/e2e/test.source \
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+ --save_path generated_summary.txt \
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+ --reference_path data/e2e/test.target \
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+ --score_path scores.txt \
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+ --bs 8
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+
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+ ```
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+
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+
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+ Please create an issue if you are facing any difficulties in replicating the results.
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+
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+ ### References
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+
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+ Please cite [[1]](https://arxiv.org/abs/1910.04073) if you found the resources in this repository useful.
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+
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+
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+ [1] Mehnaz, Laiba, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle G. Lee, Anish Acharya, and Rajiv Shah. [*GupShup: Summarizing Open-Domain Code-Switched Conversations*](https://aclanthology.org/2021.emnlp-main.499.pdf)
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+
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+
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+ ```
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+ @inproceedings{mehnaz2021gupshup,
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+ title={GupShup: Summarizing Open-Domain Code-Switched Conversations},
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+ author={Mehnaz, Laiba and Mahata, Debanjan and Gosangi, Rakesh and Gunturi, Uma Sushmitha and Jain, Riya and Gupta, Gauri and Kumar, Amardeep and Lee, Isabelle G and Acharya, Anish and Shah, Rajiv},
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+ booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
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+ pages={6177--6192},
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+ year={2021}
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+ }
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+
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+ ```