Multi review summarization












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I'm working on a project in which I want to summarize hotel reviews into a abstract summary. I've seen that RNN are the state of the art among all the abtractive summarization methods (Seq2Seq, etc). However, it performs really well on single document summarization, but to my knowledge not when the input is hundreds of reviews. I've read some papers about hierarchical Seq2seq for multi document summarization but it's only about news summarization.



So I was wondering wether you know some techniques adapted to reviews, and / or a sufficiently large dataset [multiple reviews --> summary] to train a hierarchical Seq2Seq.



Thank you !










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    $begingroup$


    I'm working on a project in which I want to summarize hotel reviews into a abstract summary. I've seen that RNN are the state of the art among all the abtractive summarization methods (Seq2Seq, etc). However, it performs really well on single document summarization, but to my knowledge not when the input is hundreds of reviews. I've read some papers about hierarchical Seq2seq for multi document summarization but it's only about news summarization.



    So I was wondering wether you know some techniques adapted to reviews, and / or a sufficiently large dataset [multiple reviews --> summary] to train a hierarchical Seq2Seq.



    Thank you !










    share|improve this question







    New contributor




    Bastien Déchamps is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















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      0





      $begingroup$


      I'm working on a project in which I want to summarize hotel reviews into a abstract summary. I've seen that RNN are the state of the art among all the abtractive summarization methods (Seq2Seq, etc). However, it performs really well on single document summarization, but to my knowledge not when the input is hundreds of reviews. I've read some papers about hierarchical Seq2seq for multi document summarization but it's only about news summarization.



      So I was wondering wether you know some techniques adapted to reviews, and / or a sufficiently large dataset [multiple reviews --> summary] to train a hierarchical Seq2Seq.



      Thank you !










      share|improve this question







      New contributor




      Bastien Déchamps is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I'm working on a project in which I want to summarize hotel reviews into a abstract summary. I've seen that RNN are the state of the art among all the abtractive summarization methods (Seq2Seq, etc). However, it performs really well on single document summarization, but to my knowledge not when the input is hundreds of reviews. I've read some papers about hierarchical Seq2seq for multi document summarization but it's only about news summarization.



      So I was wondering wether you know some techniques adapted to reviews, and / or a sufficiently large dataset [multiple reviews --> summary] to train a hierarchical Seq2Seq.



      Thank you !







      deep-learning nlp automatic-summarization






      share|improve this question







      New contributor




      Bastien Déchamps is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      Bastien Déchamps is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question






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      Bastien Déchamps is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      asked 13 hours ago









      Bastien DéchampsBastien Déchamps

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      Bastien Déchamps is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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