@inproceedings{tsai-el-ghaoui-2020-sparse,
    title = "Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm",
    author = "Tsai, Alicia  and
      El Ghaoui, Laurent",
    editor = "Moosavi, Nafise Sadat  and
      Fan, Angela  and
      Shwartz, Vered  and
      Glava{\v{s}}, Goran  and
      Joty, Shafiq  and
      Wang, Alex  and
      Wolf, Thomas",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.sustainlp-1.8/",
    doi = "10.18653/v1/2020.sustainlp-1.8",
    pages = "54--62",
    abstract = "We address the problem of unsupervised extractive document summarization, especially for long documents. We model the unsupervised problem as a sparse auto-regression one and approximate the resulting combinatorial problem via a convex, norm-constrained problem. We solve it using a dedicated Frank-Wolfe algorithm. To generate a summary with k sentences, the algorithm only needs to execute approximately k iterations, making it very efficient for a long document. We evaluate our approach against two other unsupervised methods using both lexical (standard) ROUGE scores, as well as semantic (embedding-based) ones. Our method achieves better results with both datasets and works especially well when combined with embeddings for highly paraphrased summaries."
}