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. |
Similar Papers
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. |
DialSummEval: Revisiting Summarization Evaluation for Dialogues (2022.naacl-main)
Copied to clipboard
| Challenge: | Current models for dialogue summarization have flaws that may not be well exposed by frequently used metrics such as ROUGE. |
| Approach: | They propose to re-evaluate 18 categories of metrics in terms of four dimensions: coherence, consistency, fluency and relevance, as well as a unified human evaluation of various models for the first time. |
| Outcome: | The proposed dataset will be used to evaluate 18 categories of metrics in terms of coherence, consistency, fluency and relevance, and a unified human evaluation of various models for the first time. |
FREDSum: A Dialogue Summarization Corpus for French Political Debates (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in deep learning have improved the performance of abstractive summarization systems. |
| Approach: | They present a dataset of french political debates to enhance resources for multi-lingual dialogue summarization. |
| Outcome: | The proposed dataset will be made publicly available for use by the research community. |
MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing datasets for dialogue summarization are limited to their small sizes and are built from a narrow domain. |
| Approach: | They propose a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. |
| Outcome: | The proposed dataset is larger and contains multi-party conversations from multiple domains. |
ForumSum: A Multi-Speaker Conversation Summarization Dataset (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Abstractive summarization quality has been improved but there is a lack of data for conversation summarizing applications. |
| Approach: | They propose to build a conversation summarization dataset with human written summaries from internet forums. |
| Outcome: | The proposed dataset can be easily expanded to improve conversation summarization applications. |
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization (2021.acl-long)
Copied to clipboard
| Challenge: | Existing dialogue summarization systems encode text with a number of general semantic features, but these are often not available in open-domain tools. |
| Approach: | They propose to use DialoGPT to label three types of features on two datasets . they propose to employ pre-trained and non-pre-tried models as dialogue annotators . |
| Outcome: | The proposed method improves on two dialogue summarization datasets and achieves state-of-the-art performance. |
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. |
TeSum: Human-Generated Abstractive Summarization Corpus for Telugu (2022.lrec-1)
Copied to clipboard
| Challenge: | a number of recent datasets for summarisation, scraped the web-content relying on the assumption that summary is made available with the article by the publishers. |
| Approach: | They propose a pipeline that crowd-sources summarization data and then aggressively filters the content via: automatic and partial expert evaluation. |
| Outcome: | The proposed pipeline can be applied to scraped datasets to extract better quality articles-summaries pairs. |
SumTitles: a Summarization Dataset with Low Extractiveness (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for extractive summarization of dialogue data are limited by the grammar and structure of the utterances used. |
| Approach: | They propose a low-extractive corpus of movie dialogues for abstractive text summarization . they use an alignment algorithm to construct the corpus and a baseline evaluation . |
| Outcome: | The proposed method is low-extractive and shows high performance in dialogue datasets. |
Creating a Data Set of Abstractive Summaries of Turn-labeled Spoken Human-Computer Conversations (2022.lrec-1)
Copied to clipboard
| Challenge: | Digital recorded written and spoken dialogues are becoming more available due to the growing popularity of online messenger services and chatbots. |
| Approach: | They propose to use Dutch spoken human-computer conversations, an annotation layer of turn labels, and conversational abstractive summaries of user answers to build a conversational agent. |
| Outcome: | The proposed system can be integrated into a conversational agent. |