187 citations · 1.6k across the 127 of their papers we have counts for
15 papers · 2 filters
Unsupervised Opinion Summarization as Copycat-Review Generation
Arthur Bražinskas, Mirella Lapata, Ivan Titov
Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majori…
Semantic Graph Parsing with Recurrent Neural Network DAG Grammars
Federico Fancellu, Sorcha Gilroy, Adam Lopez +1
Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction. But predicting graphs presents difficult technical challenges, so it…
Controllable Sentence Simplification: Employing Syntactic and Lexical Constraints
Jonathan Mallinson, Mirella Lapata
Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with sequence-to-sequence models which have been develo…
Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programs
Bailin Wang, Ivan Titov, Mirella Lapata
Semantic parsing aims to map natural language utterances onto machine interpretable meaning representations, aka programs whose execution against a real-world environment produces…
Text Summarization with Pretrained Encoders
Yang Liu, Mirella Lapata
Bidirectional Encoder Representations from Transformers (BERT) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural…
Informative and Controllable Opinion Summarization
Reinald Kim Amplayo, Mirella Lapata
Opinion summarization is the task of automatically generating summaries for a set of reviews about a specific target (e.g., a movie or a product). Since the number of reviews for e…
Movie Plot Analysis via Turning Point Identification
Pinelopi Papalampidi, Frank Keller, Mirella Lapata
According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure,…
Weakly Supervised Domain Detection
Yumo Xu, Mirella Lapata
In this paper we introduce domain detection as a new natural language processing task. We argue that the ability to detect textual segments which are domain-heavy, i.e., sentences…
What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks
Shashi Narayan, Shay B. Cohen, Mirella Lapata
We introduce 'extreme summarization', a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question ``What is the articl…
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…
Sentence Centrality Revisited for Unsupervised Summarization
Hao Zheng, Mirella Lapata
Single document summarization has enjoyed renewed interests in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. In this…
Data-to-text Generation with Entity Modeling
Ratish Puduppully, Li Dong, Mirella Lapata
Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are traine…
Hierarchical Transformers for Multi-Document Summarization
Yang Liu, Mirella Lapata
In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode docume…
Text Generation from Knowledge Graphs with Graph Transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan +2
Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohib…
Categorization in the Wild: Generalizing Cognitive Models to Naturalistic Data across Languages
Lea Frermann, Mirella Lapata
Categories such as animal or furniture are acquired at an early age and play an important role in processing, organizing, and communicating world knowledge. Categories exist across…