52 citations · 71 across the 10 of their papers we have counts for
14 papers
Enhancing Long Document Long Form Summarisation with Self-Planning
Xiaotang Du, Rohit Saxena, Laura Perez-Beltrachini +2
We introduce a novel approach for long context summarisation, highlight-guided generation, that leverages sentence-level information as a content plan to improve the traceability a…
Uncertainty Quantification in Retrieval Augmented Question Answering
Laura Perez-Beltrachini, Mirella Lapata
Retrieval augmented Question Answering (QA) helps QA models overcome knowledge gaps by incorporating retrieved evidence, typically a set of passages, alongside the question at test…
Leveraging Entailment Judgements in Cross-Lingual Summarisation
Huajian Zhang, Laura Perez-Beltrachini
Synthetically created Cross-Lingual Summarisation (CLS) datasets are prone to include document-summary pairs where the reference summary is unfaithful to the corresponding document…
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
Giwon Hong, Aryo Pradipta Gema, Rohit Saxena +8
Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text. However, the…
Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Tasks
Huajian Zhang, Yumo Xu, Laura Perez-Beltrachini
We study existing approaches to leverage off-the-shelf Natural Language Inference (NLI) models for the evaluation of summary faithfulness and argue that these are sub-optimal due t…
Improving User Controlled Table-To-Text Generation Robustness
Hanxu Hu, Yunqing Liu, Zhongyi Yu +1
In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof auto…
Semantic Parsing for Conversational Question Answering over Knowledge Graphs
Laura Perez-Beltrachini, Parag Jain, Emilio Monti +1
In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries…
Models and Datasets for Cross-Lingual Summarisation
Laura Perez-Beltrachini, Mirella Lapata
We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language. The corpus covers twelve lan…
Automatic Construction of Evaluation Suites for Natural Language Generation Datasets
Simon Mille, Kaustubh D. Dhole, Saad Mahamood +5
Machine learning approaches applied to NLP are often evaluated by summarizing their performance in a single number, for example accuracy. Since most test sets are constructed as an…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…
Generating Summaries with Topic Templates and Structured Convolutional Decoders
Laura Perez-Beltrachini, Yang Liu, Mirella Lapata
Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content str…
Deep Graph Convolutional Encoders for Structured Data to Text Generation
Diego Marcheggiani, Laura Perez-Beltrachini
Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a r…
Bootstrapping Generators from Noisy Data
Laura Perez-Beltrachini, Mirella Lapata
A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In th…
Analysing Data-To-Text Generation Benchmarks
Laura Perez-Beltrachini, Claire Gardent
Recently, several data-sets associating data to text have been created to train data-to-text surface realisers. It is unclear however to what extent the surface realisation task ex…