Generating summaries tailored to target characteristics
arXiv:1912.08492
Abstract
Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries. While there have been attempts to incorporate a few handpicked characteristics such as length or entities, a holistic view around these preferences is missing and crucial insights on why certain characteristics should be incorporated in a specific manner are absent. With this objective, we provide a categorization around these characteristics relevant to the task of text summarization: one, focusing on what content needs to be generated and second, focusing on the stylistic aspects of the output summaries. We use our insights to provide guidelines on appropriate methods to incorporate various classes characteristics in sequence-to-sequence summarization framework. Our experiments with incorporating topics, readability and simplicity indicate the viability of the proposed prescriptions
arXiv commentAppeared in CiCLing 2019
References in corpus (6)
- Sequence to Sequence Learning with Neural Networks
- Teaching Machines to Read and Comprehend
- A Deep Reinforced Model for Abstractive Summarization
- SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
- An Actor-Critic Algorithm for Sequence Prediction
- Multi-document abstractive summarization using ILP based multi-sentence compression