| Challenge: | Recent years have brought about interest in the task of summarizing conversation threads. |
| Approach: | They develop an email thread summarization dataset that contains human-annotated short and long email threads over a wide variety of topics. |
| Outcome: | The proposed dataset contains human-annotated short (30 words) and long (100 words) summaries of 2,549 email threads over a wide variety of topics. |
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Abstractive Meeting Summarization: A Survey (2023.tacl-1)
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| Challenge: | Recent advances in deep learning have improved language generation systems, opening the door to improved forms of abstractive summarization. |
| Approach: | They propose to use neural encoder-decoder architectures to generate abstractive meeting summarizations that are particularly well-suited for multi-party conversation. |
| Outcome: | The proposed system could be used in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. |
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining (2021.acl-long)
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| Challenge: | Abstractive text summarization has primarily focused on modeling news articles . lack of standardized datasets for summarizing online conversations is a major problem . |
| Approach: | They propose to crowdsource four new datasets for summarizing online conversations . they incorporate argument mining through graph construction to directly model issues, viewpoints, and assertions present in a conversation. |
| Outcome: | The proposed models are compared against widely-used conversation summarization datasets and show comparable or improved results. |
How Far are We from Robust Long Abstractive Summarization? (2022.emnlp-main)
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| Challenge: | Abstractive summarization has made tremendous progress in recent years . however, even under a short document setting, abstractive models often generate summaries that are repetitive, ungrammatical, and factually inconsistent with the source. |
| Approach: | They perform fine-grained human annotations to evaluate long document abstractive summarization systems and develop factual consistency metrics. |
| Outcome: | The proposed model can generate more relevant summaries but not factual ones. |
SumSurvey: An Abstractive Dataset of Scientific Survey Papers for Long Document Summarization (2024.findings-acl)
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| Challenge: | a growing need for long document summarization datasets with 16k input is causing problems. |
| Approach: | They propose to use a dataset to analyze salient information in long document summarizations. |
| Outcome: | The proposed dataset outperforms existing models and LLMs in the distribution form of salient information and the distribution of salinal information is an indicator of quality. |
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (P19-1)
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| Challenge: | Existing text summarization datasets are compiled from news articles, where summary-worthy content often appears in the beginning of input articles. |
| Approach: | They present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. |
| Outcome: | The proposed dataset is compared with existing summarization datasets and demonstrates that salient content is evenly distributed in the input. |
EntSUMv2: Dataset, Models and Evaluation for More Abstractive Entity-Centric Summarization (2023.emnlp-main)
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| Challenge: | Entity-centric summarization is a form of controllable summarizing that aims to generate a summary for a specific entity given a document. |
| Approach: | They propose to use a more abstract version of the original entity-centric ENTSUM summarization dataset to generate a shorter annotated summary for downstream users. |
| Outcome: | The proposed method is more abstract and uses supervised fine-tuning and large-scale instruction tuning to provide more specific and useful summaries for downstream users. |
Abstractive Document Summarization without Parallel Data (2020.lrec-1)
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| Challenge: | Abstractive summarization typically relies on large collections of paired articles and summaries. |
| Approach: | They propose a system that relies only on example summaries and non-matching articles . they use an unsupervised sentence extractor that selects salient sentences . |
| Outcome: | The proposed system performs well on CNN/DailyMail benchmark and automatic generating a press release from a scientific journal article. |
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (D19-54)
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| 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. |
Abstractive Document Summarization with Summary-length Prediction (2023.findings-eacl)
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| Challenge: | Existing abstractive summarization models do not consider summarizing-specific information such as the target summary length. |
| Approach: | They propose a method for enabling a model to understand summarization-specific information by predicting the summary length in the encoder and generating a summary of the predicted length in fine-tuning. |
| Outcome: | The proposed method improves ROUGE scores on the WikiHow, NYT, and CNN/DM datasets. |
mRedditSum: A Multimodal Abstractive Summarization Dataset of Reddit Threads with Images (2023.emnlp-main)
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| Challenge: | Existing summarization datasets do not cover multimodal discussions, multiple modalities, or both . mRedditSum consists of 3,033 discussion threads and images with human-written summaries. |
| Approach: | They propose a multimodal discussion summarization dataset that annotates 3,033 discussion threads with a human-written summary. |
| Outcome: | The proposed method outperforms existing models and serves as competitive baseline for future work. |