task_categories:summarization task_categories:text-generation annotations_creators:no-annotation language_creators:found multilinguality:monolingual size_categories:100K<n<1M source_datasets:original language:en license:apache-2.0 conditional-text-generation region:us
---
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license:
  - apache-2.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - summarization
  - text-generation
task_ids: []
paperswithcode_id: cnn-daily-mail-1
pretty_name: CNN / Daily Mail
tags:
  - conditional-text-generation
---


## Data Summary

Data set Alpaca-cnn-dailymail is a data set version format changed by [ccdv/cnn_dailymail](https://huggingface.co/datasets/ccdv/cnn_dailymail) to meet Alpaca fine-tuning Llama2. Only versions 3.0.0 and 2.0.0 were used for merging and as a key data set for the summary extraction task.


## Licensing Information

The Alpaca-cnn-dailymail dataset version 1.0.0 is released under the Apache-2.0 License.


## Citation Information

```
@inproceedings{see-etal-2017-get,
    title = "Get To The Point: Summarization with Pointer-Generator Networks",
    author = "See, Abigail  and
      Liu, Peter J.  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P17-1099",
    doi = "10.18653/v1/P17-1099",
    pages = "1073--1083",
    abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",
}

```

```
@inproceedings{DBLP:conf/nips/HermannKGEKSB15,
  author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
  title={Teaching Machines to Read and Comprehend},
  year={2015},
  cdate={1420070400000},
  pages={1693-1701},
  url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend},
  booktitle={NIPS},
  crossref={conf/nips/2015}
}
```


