Abstractive spoken document summarization using hierarchical model with multi-stage attention diversity optimization

Abstractive summarization is a standard task for written documents, such as news articles. Applying summarization schemes to spoken documents is more challenging, especially in situations involving human interactions, such as meetings. Here, utterances tend not to form complete sentences and sometimes contain little information. Moreover, speech disfluencies will be present as well as recognition errors for automated systems. For current attention-based sequence-to-sequence summarization systems, these additional challenges can yield a poor attention distribution over the spoken document words and utterances, impacting performance. In this work, we propose a multi-stage method based on a hierarchical encoder-decoder model to explicitly model utterance-level attention distribution at training time; and enforce diversity at inference time using a unigram diversity term. Furthermore, multitask learning tasks including dialogue act classification and extractive summarization are incorporated. The performance of the system is evaluated on the AMI meeting corpus. The inclusion of both training and inference diversity terms improves performance, outperforming current state-of-the-art systems in terms of ROUGE scores. Additionally, the impact of ASR errors, as well as performance on the multitask learning tasks, is evaluated

Unsupervised Abstractive Dialogue Summarization for Tete-a-Tetes

High-quality dialogue-summary paired data is expensive to produce and domain-sensitive, making abstractive dialogue summarization a challenging task. In this work, we propose the first unsupervised abstractive dialogue summarization model for tete-a-tetes (SuTaT). Unlike standard text summarization, a dialogue summarization method should consider the multi-speaker scenario where the speakers have different roles, goals, and language styles. In a tete-a-tete, such as a customer-agent conversation, SuTaT aims to summarize for each speaker by modeling the customer utterances and the agent utterances separately while retaining their correlations. SuTaT consists of a conditional generative module and two unsupervised summarization modules. The conditional generative module contains two encoders and two decoders in a variational autoencoder framework where the dependencies between two latent spaces are captured. With the same encoders and decoders, two unsupervised summarization modules equipped with sentence-level self-attention mechanisms generate summaries without using any annotations. Experimental results show that SuTaT is superior on unsupervised dialogue summarization for both automatic and human evaluations, and is capable of dialogue classification and single-turn conversation generation

Controllable Abstractive Dialogue Summarization with Sketch Supervision

In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control. Our model has two primary components and stages: 1) a two-stage generation strategy that generates a preliminary summary sketch serving as the basis for the final summary. This summary sketch provides a weakly supervised signal in the form of pseudo-labeled interrogative pronoun categories and key phrases extracted using a constituency parser. 2) A simple strategy to control the granularity of the final summary, in that our model can automatically determine or control the number of generated summary sentences for a given dialogue by predicting and highlighting different text spans from the source text. Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum, with as high as 50.79 in ROUGE-L score. In addition, we conduct a case study and show competitive human evaluation results and controllability to human-annotated summaries