Source-linked AI summary
Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization
Jiaao Chen, Diyi Yang
TL;DR
Dialogue summarization remains under-investigated because important information is scattered across utterances and speakers. This work extracts multiple conversational views and combines them in a multi-view sequence-to-sequence model, which outperformed prior state-of-the-art models in automatic and human evaluations.
Problem
Dialogue summarization is under-investigated despite the challenge of organizing important information scattered across utterances from different speakers.
Method
The model extracts structured topic and stage views plus global and discrete views, then combines them with a multi-view sequence-to-sequence decoder to generate summaries.
Results
The proposed methods significantly outperformed previous state-of-the-art models in automatic evaluations and human judgment.
Takeaways & Limitations
Combining diverse conversational structures, especially topic and stage views, improves dialogue summarization and highlights challenges for future research.
Takeaways & Limitations
The approach uses simple unsupervised segmentation methods because conversational structure annotations are unavailable, while referral and coreference remain challenging.
Abstract
from arXiv · showhide
Text summarization is one of the most challenging and interesting problems in NLP. Although much attention has been paid to summarizing structured text like news reports or encyclopedia articles, summarizing conversations---an essential part of human-human/machine interaction where most important pieces of information are scattered across various utterances of different speakers---remains relatively under-investigated. 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 and then utilizing a multi-view decoder to incorporate different views to generate dialogue summaries. 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. We also discussed specific challenges that current approaches faced with this task. We have publicly released our code at https://github.com/GT-SALT/Multi-View-Seq2Seq.
1 Introduction
Conversation summarization is difficult because dialogue is informal, repetitive, and structurally complex, with salient information scattered across speakers and utterances. The paper proposes combining multiple conversational views in a multi-view sequence-to-sequence model.
- Motivation: Dialogue summarization is challenging because conversations are informal, verbose, repetitive, and contain scattered salient information across speakers.Unlike structured documents, dialogues include false starts, back channeling, reconfirmations, hesitations, and interruptions.
- Research gap: Most prior conversation summarization methods did not use specific conversational structures that organize utterances into meaningful dialogues.Existing work included document summarization models and multi-sentence compression, while only a few studies used topics, dialogue acts, or key point sequences.
- Research gap: A single conversation can be represented through topic, stage, global, and discrete views, each emphasizing different conversational patterns.Topic and stage views segment conversations structurally, while global and discrete views provide coarse-grained representations.
- Contribution: The paper proposes using rich conversational structures, including topic, stage, global, and discrete views, for abstractive conversation summarization.The structured views are topic and stage; the generic views are global and discrete.
- Contribution: The proposed multi-view sequence-to-sequence model encodes different views with a conversation encoder and generates summaries with a multi-view decoder.The decoder uses multi-view attention to combine information from the encoded views.
- Evaluation: Experiments on the SAMSum conversation summarization dataset evaluated the methods quantitatively and qualitatively, alongside error analyses of current challenges.The paper reports effectiveness and discusses specific challenges faced by existing approaches.
2 Related Work
Prior dialogue summarization work largely ignored diverse conversational structures, although document summarization has extensively used sequence-to-sequence and related methods. The paper addresses this gap with a multi-view model that combines topic segments, conversational stages, dialogue overviews, and utterances.
- Document Summarization: Document summarization has extensively used abstractive sequence-to-sequence models, pointer-generator networks, and reinforcement learning.These methods target structured documents such as news reports and other long-form text.
- Dialogue Summarization: Dialogue summarization research has explored compression, turn-based hierarchical models, dialogue acts, key point sequences, and topics.These approaches encode turns or incorporate selected conversational analyses for generating summaries.
- Research gap: Prior dialogue summarization work largely ignored diverse structures such as reply relations, dialogue acts, and conversation stages.The paper identifies these omissions as a limitation of existing approaches.
- Research gap: Models using only a fixed topic view may fail to capture comprehensive and nuanced conversational structures.Information loss in the conversation encoder may also produce larger decoding errors.
- Proposed approach: The paper proposes a multi-view model using topic segments, conversational stages, dialogue overview, and utterances.These views are designed to represent different aspects of conversations for dialogue summarization.
3 Method
The method extracts four conversational views, encodes their blocks, and uses a multi-view decoder to generate dialogue summaries. Structured topic and stage views complement global and discrete views, while view attention combines their information during decoding.
- Conversation View Extraction: The model extracts topic, stage, global, and discrete views to represent different conversational structures and granularities.Topic and stage views provide structured organization, while global and discrete views offer coarse and utterance-level representations.
- Conversation View Extraction: Topic view segments conversations into consecutive topical blocks using Sentence-BERT representations and C99.C99 divides the conversation into blocks after each utterance is encoded with Sentence-BERT.
- Conversation View Extraction: Stage view uses a four-state HMM with forward-only transitions to segment conversations into ordered blocks.The HMM observes Sentence-BERT representations and imposes a fixed ordering on the inferred stages.
- Conversation Encoder: For each view, the conversation encoder represents blocks with special-token hidden states and aggregates them through LSTM layers.The final hidden state of each LSTM represents the current view before decoding.
- Multi-view Decoder: The transformer-based multi-view decoder integrates encoded views and generates summaries by assigning view importance and combining view-specific token attention.A multi-view attention layer is inserted into each transformer block; temperature sharpening can make view weights nearly one-hot as T → 0.
4 Experiments
Experiments evaluate Multi-view BART on SAMSum with ROUGE and human judgments, comparing generic, structured, and multi-view configurations. Combining conversational views improves results, while longer and more participant-rich conversations remain harder to summarize.
- Evaluation Setup: SAMSum contains 14,732 daily-topic dialogues with human-written summaries for evaluating the models.
- Evaluation Setup: Models are evaluated with ROUGE-1, ROUGE-2, and ROUGE-L on the test set, with results averaged over three runs.
- Quantitative Results: Segmenting conversations with structured views further improved performance over generic views, with topic and stage views jointly increasing ROUGE scores consistently.
- View Analysis: The topic view contributed slightly more prominently than the stage view, while their combination complemented one another despite sharing dialogue content.
- Challenges: As participant and turn counts increase, ROUGE scores decrease, indicating greater summarization difficulty for longer and more participant-rich conversations.
- Human Evaluation: Multi-view BART outperformed generic and structured single-view configurations in human evaluation, achieving the highest annotation scores.
5 Model Analysis and Discussion
The analysis organizes dialogue-summarization difficulties into challenge categories and examines model errors, showing that missing information is frequent and faithfulness remains difficult. It also identifies relationships between specific challenges and errors.
- Challenge Analysis: The challenge taxonomy includes informal language, multiple participants, multiple turns, referral and coreference, repetition and interruption, negations and rhetorical questions, and role and language change.These challenges involve noisy language, speaker differentiation, longer conversations, scattered information, discourse interruptions, negation, and shifting speaker roles.
- Challenge Analysis: 100 sampled conversations were organized into seven challenge categories, with conversations allowed to receive multiple labels.Examples without listed challenges were labeled Generic and were usually shorter or simpler.
- Challenge Analysis: Referral & coreference (33%) and Role & language change (30%) were the two most frequent challenges.The best model performed relatively worse on Repetition & interruption and Multiple turns.
- Error Analysis: The analyzed error types included missing information, redundancy, wrong references, incorrect reasoning, and improper gendered pronouns.Wrong references associate actions or locations with the wrong speaker, while incorrect reasoning produces wrong dialogue conclusions.
- Error Analysis: Missing information (37%) was the most frequent model error, while incorrect reasoning occurred in 24% of sampled summaries and had the worst ROUGE-2.Improper gendered pronouns were a minor error type at 6% but appeared to substantially reduce ROUGE-1 and ROUGE-2.
- Challenge–Error Relations: All challenge types correlated highly with missing-information errors, while wrong references were highly associated with referral & coreference.The model generated good summaries for generic, simple conversations.
6 Conclusion
The paper proposes a multi-view sequence-to-sequence model that uses conversational and generic views to generate dialogue summaries. Experiments support its effectiveness, while limited annotations constrain view extraction and motivate supervised segmentation and richer structures.
- Conclusion: The model uses topic, stage, global, and discrete views to represent conversations and generate summaries.The topic and stage views capture conversational structures, while global and discrete views provide generic representations.
- Conclusion: Experiments demonstrated effectiveness through both quantitative and qualitative evaluations.The paper also reports error analyses identifying challenges for current dialogue-summarization models.
- Conclusion: Because annotations were unavailable, the study used simple unsupervised segmentation methods to extract different views.Future work includes annotated data, supervised segmentation models, and additional conversation structures such as dialogue acts.
A Model Settings
The model settings use sentence-BERT representations and fixed segmentation hyperparameters for topic and stage views. Experiments were run on two Tesla P100 GPUs.
- Representation and Segmentation: Sentence-BERT with the pre-trained bert-base-nli-stsb-mean-tokens model generated representations for each utterance.Topic-view extraction used window size 4 and standard-deviation coefficient 1 in C99.
- Representation and Segmentation: Stage-view extraction used four hidden states in an HMM, with hyperparameters selected through grid search and human evaluation of sampled segmentations.Structured-view and generic-view BART models used the same parameters.
- Hardware: Experiments were performed on two Tesla P100 GPUs with 16GB memory each.
B View Attention Visualization
Attention visualization compares the model’s topic and stage views. Topic views received greater attention, while combining both views produced more precise summaries and balanced their individual advantages.
- Attention Visualization: Figure 6 visualizes attention-weight distributions for the topic view and stage view in the best multi-view model.
- Attention Visualization: Topic views were more prominent than stage views, consistent with the separate topic-view and stage-view model performances.
- Combined Views: Both topic and stage views could improve conversation summarization, and the two structured views complemented each other despite sharing the same dialogue content.
- Combined Views: Combined views balanced the advantages of individual views and generated more precise summaries in the examples shown.The learned attention weights were consistent with the single-view performances.
C Supplementary Examples for Model Analysis and Discussion
The supplementary analysis compares generated summaries and error patterns across models on 100 sampled SAMSum test conversations. Multi-view modeling produced the fewest errors, while explicit topic and stage structures reduced redundancy and incorrect reasoning.
- Analysis setup: 100 sampled conversations from the SAMSum test set were used for the model analysis and discussion.The sample index is provided in Table 7.
- Generated examples: Table 8 compares generated summaries with references using Rouge-1/Rouge-2/Rouge-L scores and displays stage and topic weights.
- Error analysis: Multi-view models made the fewest errors, while topic-view and stage-view models reduced redundancy and incorrect reasoning compared with discrete-view and global-view models.The analysis compares BART-Discrete, BART-Global, BART-Stage, BART-Topic, and BART-Multi-view against golden references.