RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to learn vital information from dialogue context with limited data are limited due to limited words in utterances and huge gap between dialogue and its summary. |
| Approach: | They propose an unsupervised strategy to learn vital information from dialogue context . the proposed model uses a hypothetical foundation that a superior summary approximates a replacement of the original dialogue . |
| Outcome: | The proposed model outperforms existing models on a number of datasets. |
Similar Papers
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (D19-54)
Copied to clipboard
| Challenge: | Existing work on abstractive dialogue summarizations has focused on news summarizing but there is no such comprehensive dataset. |
| Approach: | They propose to use a chat-dialogues corpus with abstractive dialogue summaries to generate a short version of text that covers the main points succinctly. |
| Outcome: | The proposed dataset achieves higher ROUGE scores than the model-generated summaries of news, compared with human evaluators' judgement. |
Leveraging Summarization for Unsupervised Dialogue Topic Segmentation (2024.findings-naacl)
Copied to clipboard
Aleksei Artemiev, Daniil Parinov, Alexey Grishanov, Ivan Borisov, Alexey Vasilev, Daniil Muravetskii, Aleksey Rezvykh, Aleksei Goncharov, Andrey Savchenko
| Challenge: | Existing methods to segment textual data are difficult to handle for noisy spoken dialogues. |
| Approach: | They propose to leverage dialogue summaries for unsupervised topic segmentation . they show that the new approach outperforms state-of-the-art methods in unsupervised segmentation and requires less setup . |
| Outcome: | The proposed approach outperforms state-of-the-art methods in unsupervised topic segmentation and requires less setup. |
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing models focus more on the structure of summary, not on the personal and logical inconsistency problem. |
| Approach: | They propose a model to solve the problem of personal and logical inconsistency . they use an utterance rewriter to complete the ellipsis content of dialogue content . |
| Outcome: | The proposed model outperforms baseline models on both SAMSum and DialSum datasets. |
A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Abstractive summarization models have achieved impressive results on document summarizing tasks, but their performance on dialogue modeling is poor due to the crude and straight methods for dialogue encoding. |
| Approach: | They propose a model that leverages Finer-grain universal Dialogue semantic Structures to model dialogue and generate better summaries. |
| Outcome: | The proposed model outperforms various dialogue summarization approaches and achieves state-of-the-art (SOTA) ROUGE results on a SAMsum dataset. |
DialogSum: A Real-Life Scenario Dialogue Summarization Dataset (2021.findings-acl)
Copied to clipboard
| Challenge: | Experimental results show unique challenges in dialogue summarization such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense. |
| Approach: | They propose a large-scale labeled dialogue summarization dataset . they use state-of-the-art neural models to analyze spoken dialogue summaries . |
| Outcome: | The proposed dataset can be used to analyze spoken dialogue summarization challenges. |
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization (2022.coling-1)
Copied to clipboard
| Challenge: | Existing frameworks that use commonsense as supervision only use input knowledge, but it generates more informative and consistent summaries. |
| Approach: | They propose to leverage the unique characteristics of dialogues sharing commonsense knowledge to solve the difficulties in summarizing them. |
| Outcome: | The proposed framework generates more informative and consistent summaries with injected commonsense knowledge than existing methods. |
Data Augmentation for Low-Resource Dialogue Summarization (2022.findings-naacl)
Copied to clipboard
| Challenge: | DADS generates synthetic examples by replacing sections of text from input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the simulated dialogue. |
| Approach: | They propose a Data Augmentation technique for low-resource Dialogue Summarization that uses pretrained language models to generate diverse alternatives. |
| Outcome: | The proposed method generates synthetic examples from a low-resource dataset . it produces topically diverse examples without introducing additional hallucinations . |
Controllable Abstractive Dialogue Summarization with Sketch Supervision (2021.findings-acl)
Copied to clipboard
| Challenge: | Using a model to generate summary sketches, we improve abstractive dialogue summarization quality and enable granularity control. |
| Approach: | They propose a model that generates a preliminary summary sketch and a strategy to control granularity. |
| Outcome: | The proposed model achieves state-of-the-art on the largest dialogue summarization corpus with as high as 50.79 in ROUGE-L score. |
RefSum: Refactoring Neural Summarization (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing methods for text summarization are limited by limitations of reranking or stacking. |
| Approach: | They propose a framework that provides a unified view of text summarization and summaries combination. |
| Outcome: | The proposed method can be used by researchers as an off-the-shelf tool to achieve further performance improvements. |
Guiding Abstractive Dialogue Summarization with Content Planning (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for abstractive dialogue summarization struggle to maintain factual consistency between dialogue and summary. |
| Approach: | They propose a coarse-to-fine model for generating abstractive dialogue summaries and introduce a fact-aware reinforcement learning objective that improves the fact consistency between the dialogue and the generated summary. |
| Outcome: | The proposed model improves the quality of the generated summary, especially in coherence and consistency. |