This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation (P19-1)
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| Challenge: | Existing research tracks on email use focus on email summarization, email keyword extraction and action detection. |
| Approach: | They propose to use email body to automatically generate an email subject line from the email body. |
| Outcome: | The proposed method outperforms baselines and state-of-the-art systems in the evaluation of human and automatic metrics. |
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EmailSum: Abstractive Email Thread Summarization (2021.acl-long)
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| 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. |
Updated Headline Generation: Creating Updated Summaries for Evolving News Stories (2022.acl-long)
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| Challenge: | Existing systems that generate headlines for updated articles are not as efficient as static ones. |
| Approach: | They propose a task where a system generates a headline for an updated article, considering both the previous article and headline. |
| Outcome: | The proposed model produces headlines judged by humans to be as factual as gold headlines while making fewer unnecessary edits compared to a standard headline generation model. |
Towards Content Transfer through Grounded Text Generation (N19-1)
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| Challenge: | Recent work in neural natural language generation has attracted significant interest in controlling the form of text, such as style, persona, and wordiness. |
| Approach: | They propose a task where the task is to generate a next sentence in a document that fits its context and is grounded in . external textual source such as a news story. |
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Improving Abstraction in Text Summarization (D18-1)
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| Challenge: | Abstractive text summarization models do not capture the abstractive nature of high quality summaries. |
| Approach: | They propose to decompose a decoder into a contextual network and a pretrained language model that incorporates prior knowledge about language generation. |
| Outcome: | The proposed model achieves comparable results to state-of-the-art models, based on ROUGE scores and human evaluations, while producing a significantly higher level of abstraction. |
End-to-End Segmentation-based News Summarization (2022.findings-acl)
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| Challenge: | Existing summarization systems only provide one genetic summary of the whole article, making it difficult for users to navigate the reading. |
| Approach: | They propose a task of segmenting a news article into multiple sections and generating the corresponding summary to each section. |
| Outcome: | The proposed model outperforms state-of-the-art models on a 27k news article dataset . it can jointly segment a document and produce the summary for each section . |
Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning (D19-1)
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| Challenge: | Existing automated generation of articles' characteristics is inconsistent if they are generated individually. |
| Approach: | They propose a multi-task learning model with a shared encoder and multiple decoders for each task. |
| Outcome: | The proposed model generates more consistent headlines, key phrases and categories . it outperforms baseline model on the ROUGE scores and generates fluent headlines . |
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)
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| Challenge: | Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
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SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)
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Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | a lack of comprehensive studies on evaluation metrics for text summarization hinders progress . a new study aims to improve evaluation metrics that correlate with human judgments . |
| Approach: | They propose to re-evaluate automatic evaluation metrics and share a toolkit for evaluation . they hope to promote a more complete evaluation protocol for text summarization . |
| Outcome: | The proposed evaluation metrics are inconsistent with existing evaluation protocols. |
Inducing Document Structure for Aspect-based Summarization (P19-1)
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| Challenge: | Abstractive summarization systems treat documents as unstructured and generate a single generic summary per document. |
| Approach: | They propose to incorporate document structure into automatic summarization systems . they induce latent document structure and abstractive summarizing objective . |
| Outcome: | The proposed model improves on topic-agnostic baselines and can produce abstractive and extractive aspect-based summaries. |
Content Selection in Deep Learning Models of Summarization (D18-1)
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| Challenge: | Using deep learning models, we find that word embedding does not improve performance over simpler models. |
| Approach: | They propose to use sentence embedding to perform content selection across multiple domains . they propose to propose two alternative models that use auto-regressive sentence extraction . |
| Outcome: | The proposed models improve performance across news, personal stories, meetings, and medical articles. |