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Found 12 papers, 8 papers with code
EntSUM: A Data Set for Entity-Centric Extractive Summarization
Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document. We introduce a human-annotated data set EntSUM for controllable summarization with a focus on named entities as the aspects to control. We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries.
Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts
Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format.
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA
Large language models (e. g., GPT-4) are uniquely capable of producing highly rated text simplification, yet current human evaluation methods fail to provide a clear understanding of systems' specific strengths and weaknesses.
LENS: A Learnable Evaluation Metric for Text Simplification
Training learnable metrics using modern language models has recently emerged as a promising method for the automatic evaluation of machine translation.
EntSUM: A Data Set for Entity-Centric Summarization
Our analysis and results show the challenging nature of this task and of the proposed data set.
BiSECT: Learning to Split and Rephrase Sentences with Bitexts
We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited.
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
Controllable Text Simplification with Explicit Paraphrasing
Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting.
Neural CRF Model for Sentence Alignment in Text Simplification
The success of a text simplification system heavily depends on the quality and quantity of complex-simple sentence pairs in the training corpus, which are extracted by aligning sentences between parallel articles.
Ranked #1 on
Text Simplification
on Newsela
Code and Named Entity Recognition in StackOverflow
We also present the SoftNER model which achieves an overall 79. 10 F$_1$ score for code and named entity recognition on StackOverflow data.
Multi-task Pairwise Neural Ranking for Hashtag Segmentation
Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics.
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
Current lexical simplification approaches rely heavily on heuristics and corpus level features that do not always align with human judgment.
