A Bag of Tricks for Dialogue Summarization

• URL: http://arxiv.org/abs/2109.08232v1
• Date: Thu, 16 Sep 2021 21:32:02 GMT
• Title: A Bag of Tricks for Dialogue Summarization
• Authors: Muhammad Khalifa, Miguel Ballesteros, Kathleen McKeown
• Abstract summary: We explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data.
• Score: 7.7837843673493685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dialogue summarization comes with its own peculiar challenges as opposed to news or scientific articles summarization. In this work, we explore four different challenges of the task: handling and differentiating parts of the dialogue belonging to multiple speakers, negation understanding, reasoning about the situation, and informal language understanding. Using a pretrained sequence-to-sequence language model, we explore speaker name substitution, negation scope highlighting, multi-task learning with relevant tasks, and pretraining on in-domain data. Our experiments show that our proposed techniques indeed improve summarization performance, outperforming strong baselines.

Related papers

• Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models [52.24756457516834]
  We propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of Large Language Models (LLMs) This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts.
  arXiv  Detail & Related papers  (2023-09-22T15:41:34Z)
• Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning [27.92734269206744]
  InstructDial is an instruction tuning framework for dialogue. It consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting.
  arXiv  Detail & Related papers  (2022-05-25T11:37:06Z)
• UniDU: Towards A Unified Generative Dialogue Understanding Framework [62.8474841241855]
  We investigate a unified generative dialogue understanding framework, namely UniDU, to achieve information exchange among DU tasks. We conduct experiments on ten dialogue understanding datasets, which span five fundamental tasks. The proposed UniDU framework outperforms task-specific well-designed methods on all 5 tasks.
  arXiv  Detail & Related papers  (2022-04-10T09:32:34Z)
• Who says like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning [2.251583286448503]
  We focus on the association between utterances from individual speakers and unique syntactic structures. Speakers have unique textual styles that can contain linguistic information, such as voiceprint. We employ multi-task learning of both syntax-aware information and dialogue summarization.
  arXiv  Detail & Related papers  (2021-09-29T05:30:39Z)
• Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization [41.75442239197745]
  This work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives. Experiments on benchmark datasets demonstrate that the proposed simple method significantly outperforms strong baselines.
  arXiv  Detail & Related papers  (2021-09-10T17:03:25Z)
• Coreference-Aware Dialogue Summarization [24.986030179701405]
  We investigate approaches to explicitly incorporate coreference information in neural abstractive dialogue summarization models. Experimental results show that the proposed approaches achieve state-of-the-art performance. Evaluation results on factual correctness suggest such coreference-aware models are better at tracing the information flow among interlocutors.
  arXiv  Detail & Related papers  (2021-06-16T05:18:50Z)
• Structural Pre-training for Dialogue Comprehension [51.215629336320305]
  We present SPIDER, Structural Pre-traIned DialoguE Reader, to capture dialogue exclusive features. To simulate the dialogue-like features, we propose two training objectives in addition to the original LM objectives. Experimental results on widely used dialogue benchmarks verify the effectiveness of the newly introduced self-supervised tasks.
  arXiv  Detail & Related papers  (2021-05-23T15:16:54Z)
• Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning [14.239355474794142]
  We present a multi-task learning framework to enable the training of one universal incremental dialogue processing model. We show that these tasks provide positive inductive biases to each other with the optimal contribution of each one relying on the severity of the noise from the task.
  arXiv  Detail & Related papers  (2020-11-13T04:31:51Z)
• Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization [72.54873655114844]
  Text summarization is one of the most challenging and interesting problems in NLP. This work proposes a multi-view sequence-to-sequence model by first extracting conversational structures of unstructured daily chats from different views to represent conversations. Experiments on a large-scale dialogue summarization corpus demonstrated that our methods significantly outperformed previous state-of-the-art models via both automatic evaluations and human judgment.
  arXiv  Detail & Related papers  (2020-10-04T20:12:44Z)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)
• Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning [50.5572111079898]
  Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks.
  arXiv  Detail & Related papers  (2020-02-27T04:36:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.