Predicting Discourse Trees from Transformer-based Neural Summarizers (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing extractive summarization tasks use only neural approaches to learn discourse information, but recent work has shown that it is beneficial for summarizing discourse information. |
| Approach: | They propose to generate document-level discourse trees from pre-trained neural summarizers that encode dependency- and constituency-style discourse information. |
| Outcome: | The proposed model learns both, dependency- and constituency-style discourse information, consistent with pre-neural results. |
Similar Papers
Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)
Copied to clipboard
| 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. |
Discourse-Aware Neural Extractive Text Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Recent studies have shown that sentence-based extractive models result in redundant or uninformative phrases in the extracted summaries. |
| Approach: | They propose a discourse-aware neural summarization model that extracts sub-sentential discourse units as candidates for extractive selection on a finer granularity. |
| Outcome: | Experiments show that the proposed model outperforms state-of-the-art models on popular summarization benchmarks. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Discourse Representation Structure Parsing (P18-1)
Copied to clipboard
| Challenge: | Existing semantic parsers are data-driven using annotated examples consisting of utterances and their meaning representations. |
| Approach: | They propose a method which transforms Discourse Representation Structures (DRSs) to trees and develop a structure-aware model which decomposes the decoding process into three stages. |
| Outcome: | The proposed model outperforms baseline models on the Groningen Meaning Bank (GMB) by a wide margin. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
Copied to clipboard
| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
| Outcome: | The proposed parser outperforms existing models on two key RST datasets and on large-scale "silver-standard" discourse treebank MEGA-DT. |
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
Copied to clipboard
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
| Challenge: | Existing abstractive summarization models focus on summarizing sentences and short documents. |
| Approach: | They propose a hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models on two large-scale datasets of scientific papers. |
Evaluating Discourse in Structured Text Representations (P19-1)
Copied to clipboard
| Challenge: | Discourse structure is integral to understanding a text and is useful in many NLP tasks. |
| Approach: | They propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. |
| Outcome: | The proposed model improves performance on multiple discourse-relevant tasks and datasets and ablation studies show it does little to capture discourse structure. |
Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains (D19-1)
Copied to clipboard
| Challenge: | Discourse relation classification is one of the most difficult tasks in discourse parsing. |
| Approach: | They propose a bidirectional encoder representation from transformer model that encodes a representation of likely next sentences. |
| Outcome: | The proposed model outperforms the state-of-the-art system in 11-way classification by 8% points on the standard PDTB dataset. |
Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Contemporary leading-edge systems for abstractive (long) text summarization employ Transformer encoderdecoder architectures that only consider the nuclearity annotation . |
| Approach: | They propose to incorporate Rhetorical Structure Theory into a novel summarization model that incorporates both the types and uncertainty of rhetorical relations. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation. |
Enriching and Controlling Global Semantics for Text Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document. |
| Approach: | They propose a neural topic model empowered with normalizing flow to capture global semantics of the document and integrate them into the summarization model. |
| Outcome: | The proposed model outperforms state-of-the-art summarization models on five common text summarizing datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed. |