aquamuse

References:

• Code
• Huggingface

abstractive

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:aquamuse/abstractive')

• Description:

AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)

• License: No known license
• Version: 2.3.0
• Splits:

• Features:

{
    "query": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "input_urls": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "target": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}

extractive

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:aquamuse/extractive')

• Description:

AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)

• License: No known license
• Version: 2.3.0
• Splits:

• Features:

{
    "query": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "input_urls": {
        "feature": {
            "dtype": "string",
            "id": null,
            "_type": "Value"
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    },
    "target": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    }
}