| Challenge: | a new dataset for tweet summarization is available for free. |
| Approach: | They propose a dataset for tweet summarization that uses human annotations to evaluate extractive summarizing methods. |
| Outcome: | The proposed dataset includes six events collected from Twitter . human-annotated gold-standard references facilitate evaluation, the study shows . |
Similar Papers
TWEETSUM: Event oriented Social Summarization Dataset (2020.coling-main)
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| Challenge: | Developing social summarization systems is becoming more and more critical . but, the publicly available and high-quality large scale social summaries are rare . |
| Approach: | They propose to build a social summarization dataset using twitter's hot events . they collect user relations, hashtags and user profiles to evaluate their summarizing methods . |
| Outcome: | The proposed dataset is based on a dataset from twitter with 12 real world hot events with 44,034 tweets and 11,240 users. |
WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation (2021.acl-short)
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| Challenge: | Existing summarization datasets are limited in their ability to evaluate output . a human evaluation is necessary to understand and improve summarizing systems . |
| Approach: | They propose a dataset based on how-to articles and coherent paragraph summaries written in plain language. |
| Outcome: | The proposed dataset makes human evaluation easier and more effective . the authors compare the proposed dataset to existing ones on PubMed and the literature. |
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (2020.findings-emnlp)
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| Challenge: | Modern NLP systems are typically ill-equipped when applied to noisy user-generated text. |
| Approach: | They propose a new evaluation framework consisting of seven Twitter-specific classification tasks. |
| Outcome: | The proposed framework is based on seven heterogeneous Twitter-specific classification tasks. |
SumPubMed: Summarization Dataset of PubMed Scientific Articles (2021.acl-srw)
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| Challenge: | Existing summarization models that can extract the top few lines of news articles fail to summarize long documents. |
| Approach: | They constructed a scientific summarization dataset from MEDLINE articles from the PubMed archive to address this problem. |
| Outcome: | The proposed model outperforms existing models on news article summarization datasets and shows that it is more efficient to extract the top few lines. |
The State and Fate of Summarization Datasets: A Survey (2025.naacl-long)
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| Challenge: | Summarization is the task of shortening a text while preserving the most important information it contains. |
| Approach: | They propose a novel ontology covering sample properties, collection methods and distribution covering sample characteristics, collection method and distribution. |
| Outcome: | The proposed ontology covers sample properties, collection methods and distribution, and can be used to streamline future research into a more coherent body of work. |
ForumSum: A Multi-Speaker Conversation Summarization Dataset (2021.findings-emnlp)
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| 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. |
Creation and evaluation of timelines for longitudinal user posts (2023.eacl-main)
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| Challenge: | Existing methods for segmenting user posts into timelines improve quality and cost of manual annotation. |
| Approach: | They propose a set of methods for segmenting longitudinal user posts into timelines likely to contain interesting moments of change in a user’s behaviour based on their online posting activity. |
| Outcome: | The proposed framework is able to evaluate two different social media datasets and compares with existing models. |
Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects - A Survey (2024.findings-acl)
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| Challenge: | scholarly attention has turned to the development of text summarization methods that are more closely tailored and controlled to align with specific objectives and user needs. |
| Approach: | They formalize a controllable text summarization task and categorize controllability attributes according to their shared characteristics and objectives. |
| Outcome: | The proposed method is tailored to meet the specific intent and needs of users. |
EntSUM: A Data Set for Entity-Centric Extractive Summarization (2022.acl-long)
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| Challenge: | Existing methods for controllable summarization fail to generate entity-centric summaries. |
| Approach: | They propose to use a human-annotated data set EntSUM to generate controllable summarization with a focus on named entities as the aspects to control. |
| Outcome: | The proposed data set shows that existing methods fail to generate entity-centric summaries. |
What Have We Achieved on Text Summarization? (2020.emnlp-main)
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| Challenge: | Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals. |
| Approach: | They analyze 8 major sources of errors on 10 representative summarization models manually. |
| Outcome: | Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models. |