Papers with content selection
GRUEN for Evaluating Linguistic Quality of Generated Text (2020.findings-emnlp)
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| Challenge: | Existing evaluation metrics focus on content selection, not linguistic quality . proposed GRUEN measures Grammaticality, non-redundancy, focUs, structure and coherence of generated text. |
| Approach: | They propose to use a BERT-based model and a class of syntactic, semantic, and contextual features to examine the system output. |
| Outcome: | Experiments show that the proposed metric correlates highly with human judgments. |
Enhanced Transformer Model for Data-to-Text Generation (D19-56)
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| Challenge: | Neural models have shown significant progress on data-to-text generation tasks . data- to-text models generate descriptive texts from non-linguistic structured data . |
| Approach: | They propose a new data-to-text generation model which learns content selection and summary generation in an end-to end fashion. |
| Outcome: | The proposed model outperforms current state-of-the-art models on content selection precision and content ordering metrics. |
Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)
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| Challenge: | Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. |
| Approach: | They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. |
| Outcome: | The proposed method improves extractive summarization performance on CNN/Daily Mail dataset. |
Few-Shot NLG with Pre-Trained Language Model (2020.acl-main)
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| Challenge: | Neural-based approaches to natural language generation are data-hungry and difficult to adopt in real-world applications. |
| Approach: | They propose a task of few-shot natural language generation from structured data or knowledge to generate coherent sentences from input data and language modeling to compose coherent sentences. |
| Outcome: | The proposed approach outperforms the strongest baseline approach by over 8.0 BLEU points improvement. |
SYSTRAN @ WNGT 2019: DGT Task (D19-56)
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| Challenge: | SYSTRAN participates in Document-level generation and trans-lation (DGT) task . data-to-text generation tasks are difficult because of the content selection and text generation data. |
| Approach: | They propose a Transformer-based datato-text generation model which jointly learns content selection and text generation. |
| Outcome: | The proposed model outperforms current state-of-the-art system on BLEU, content selection precision and content ordering metics. |
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion (2020.aacl-main)
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| Challenge: | Existing systems that perform content selection and surface realization are not able to provide sufficient training data for news summarization. |
| Approach: | They propose to use a cascade architecture to perform content selection and surface realization together to generate abstracts. |
| Outcome: | The proposed architecture outperforms or outranks existing systems in terms of content selection and surface realization. |
Select and Attend: Towards Controllable Content Selection in Text Generation (D19-1)
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| Challenge: | Recent neural network models conflate content selection and surface realization into a black-box architecture, resulting in content to be described in text cannot be explicitly controlled. |
| Approach: | They propose to decouple content selection from the decoder to allow finer-grained control over the generation. |
| Outcome: | The proposed model can be trained end-to-end without human annotations and achieves promising results in data-totext and headline generation tasks. |
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. |
| Outcome: | The proposed model outperforms competing models in three domains with diverse topics and varying language styles. |
CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization (2025.findings-naacl)
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| Challenge: | Recent efforts to produce concise legal summarization have shifted towards abstractive approaches . |
| Approach: | They propose a framework that integrates content selection and planning components to generate coherent summaries based on both the content and the structured plan. |
| Outcome: | The proposed framework shows that it integrates content selection and planning components over entity-centric approaches in the context of legal judgements. |
Training Dynamics for Text Summarization Models (2022.findings-acl)
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| Challenge: | Pre-trained language models have shown impressive results when fine-tuned on large summarization datasets. |
| Approach: | They analyze the training dynamics for generation models, focusing on summarization . they find that a propensity to copy the input is learned early in the training process . |
| Outcome: | The proposed model learns at different stages of fine-tuning, the authors show . they show that factual errors are learnt in later stages, but not at high-loss tokens . |
Attribute First, then Generate: Locally-attributable Grounded Text Generation (2024.acl-long)
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| Challenge: | Recent efforts to address hallucinations in Large Language Models have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections. |
| Approach: | They propose a locally-attributable text generation approach prioritizing concise attributions by identifying relevant source segments and conditioning the generation process on them. |
| Outcome: | The proposed method yields more concise citations than baselines and significantly reduces time required for fact verification by human assessors. |
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. |
The NLP Task Effectiveness of Long-Range Transformers (2023.eacl-main)
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| Challenge: | Existing benchmarks on long-range attention models have not been sufficient to develop efficient Transformers and their practical application on complex NLP tasks. |
| Approach: | They propose to benchmark 7 Transformer variants on 5 difficult NLP tasks and 7 datasets to examine their capacity for long-range attention. |
| Outcome: | The proposed models have advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error. |
Routing Enforced Generative Model for Recipe Generation (2020.emnlp-main)
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| Challenge: | Existing methods to generate recipes with information about ingredients are difficult to use in practice. |
| Approach: | They propose a routing method to dive into the content selection under the internal restrictions. |
| Outcome: | The proposed model improves on BLEU, F1 and human evaluation. |
Stepwise Extractive Summarization and Planning with Structured Transformers (2020.emnlp-main)
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| Challenge: | Existing approaches to extractive summarization use transformers to learn the structure of long inputs. |
| Approach: | They propose encoder-centric stepwise models for extractive summarization using structured transformers – HiBERT and Extended Transformers . |
| Outcome: | The proposed models outperform previous models on CNN/DailyMail extractive summarization and Rotowire table-to-text generation. |
Bias in News Summarization: Measures, Pitfalls and Corpora (2024.findings-acl)
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| Challenge: | Pretrained large language models can reproduce harmful social biases in constrained settings, such as summarization. |
| Approach: | They propose a method to generate input documents with carefully controlled demographic attributes and then apply it to a controlled setting. |
| Outcome: | The proposed method allows to generate input documents with carefully controlled demographic attributes while working with real-world input documents. |
Summary Cloze: A New Task for Content Selection in Topic-Focused Summarization (D19-1)
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| Challenge: | Recent work on topic-focused summarization has focused on generic summarizing due to the size of the available datasets. |
| Approach: | They propose a method to study content selection in topic-focused summarization by narrowing the scope to a sentence-level task. |
| Outcome: | The proposed method can generate a large-scale summary cloze dataset from Wikipedia. |
Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions (2025.findings-emnlp)
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| Challenge: | FRAME reframes summarization as a semantic enrichment task . SCOPE is a reason-out-loud protocol that has the model build a reasoning trace . |
| Approach: | They propose a modular pipeline that reframes summarization as a semantic enrichment task. |
| Outcome: | The proposed pipeline reduces hallucinations and omissions by 2 out of 5 points . SCOPE improves knowledge fit and goal alignment over prompt-only baselines . |
Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning (2025.emnlp-main)
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| Challenge: | Existing methods for extracting sentences from documents leave some implicit discourse information in the sentence unresolved due to their lack of context. |
| Approach: | They propose a content selection framework for zero-shot decontextualisation which determines what content should be mentioned and in what order for a sentence to be understood out of context. |
| Outcome: | The proposed framework outperforms existing methods in rewriting sentences that lack context while maintaining original meaning. |