An official website of the United States government
Abstract—Summarization of long sequences into a concise statement is a core problem in natural language processing, which requires a non-trivial understanding of the weakly structured text. Therefore, integrating crowdsourced multiple users’ comments into a concise summary is even harder because (1) it requires transferring the weakly structured comments to structured knowledge. Besides, (2) the users comments are informal and noisy. In order to capture the long-distance relationships in staggered long sentences, we propose a neural multi-comment summarization (MCS) system that incorporates the sentence relationships via graph heuristics that utilize relation knowledge graphs, i.e., sentence relation graphs (SRG) and approximate discourse graphs (ADG). Motivated by the promising results of gated graph neural networks (GG-NNs) on highly structured data, we develop a GG-NNs with sequence encoder that incorporates SRG or ADG in order to capture the sentence relationships. Specifically, we employ the GG-NNs on both relation knowledge graphs, with the sentence embeddings as the input node features and the graph heuristics as the edges’ weights. Through multiple layerwise propagations, the GG-NNs generate the salience for each sentence from high-level hidden sentence features. Consequently, we use a greedy heuristic to extract salient users’ comments while avoiding the noise in comments. The experimental results show that the proposed MCS improves the summarization performance both quantitatively and qualitatively.
more »
« less
Multiplex Graph Neural Network for Extractive Text Summarization
Extractive text summarization aims at extract- ing the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sen- tence embedding plays an important role. Re- cent studies have leveraged graph neural net- works to capture the inter-sentential relation- ship (e.g., the discourse graph) to learn con- textual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity & natural connection), nor model intra-sentential relationships (e.g, semantic & syntactic relationship among words). To ad- dress these problems, we propose a novel Mul- tiplex Graph Convolutional Network (Multi- GCN) to jointly model different types of rela- tionships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extrac- tive text summarization. Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate the effec- tiveness of our method.
more »
« less
- PAR ID:
- 10332513
- Date Published:
- Journal Name:
- EMNLP
- Page Range / eLocation ID:
- 133 to 139
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
For decades, research in natural language processing (NLP) has focused on summarization. Sequence-to-sequence models for abstractive summarization have been studied extensively, yet generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, summarizers need to acquire the co-references that form multiple types of relations over input sentences, e.g., 1-to-N, N-to-1, and N-to-N relations, since the structured knowledge for text usually appears on these relations. By allowing the decoder to pay different attention to the input sentences for the same entity at different generation states, the structured graph representations generate more informative summaries. In this paper, we propose a hierarchical graph attention networks (HGATs) for abstractive summarization with a topicsensitive PageRank augmented graph. Specifically, we utilize dual decoders, a sequential sentence decoder, and a graph-structured decoder (which are built hierarchically) to maintain the global context and local characteristics of entities, complementing each other. We further design a greedy heuristic to extract salient users’ comments while avoiding redundancy to drive a model to better capture entity interactions. Our experimental results show that our models produce significantly higher ROUGE scores than variants without graph-based attention on both SSECIF and CNN/Daily Mail (CNN/DM) datasets.more » « less
-
null (Ed.)Named entity recognition (NER) is a fundamental task in the natural language processing (NLP) area. Recently, representation learning methods (e.g., character embedding and word embedding) have achieved promising recognition results. However, existing models only consider partial features derived from words or characters while failing to integrate semantic and syntactic information (e.g., capitalization, inter-word relations, keywords, lexical phrases, etc.) from multi-level perspectives. Intuitively, multi-level features can be helpful when recognizing named entities from complex sentences. In this study, we propose a novel framework called attention-based multi-level feature fusion (AMFF), which is used to capture the multi-level features from different perspectives to improve NER. Our model consists of four components to respectively capture the local character-level, global character-level, local word-level, and global word-level features, which are then fed into a BiLSTM-CRF network for the final sequence labeling. Extensive experimental results on four benchmark datasets show that our proposed model outperforms a set of state-of-the-art baselines.more » « less
-
Carlotta Demeniconi, Ian Davidson: (Ed.)Multi-document summarization, which summarizes a set of documents with a small number of phrases or sentences, provides a concise and critical essence of the documents. Existing multi-document summarization methods ignore the fact that there often exist many relevant documents that provide surrounding background knowledge, which can help generate a salient and discriminative summary for a given set of documents. In this paper, we propose a novel method, SUMDocS (Surrounding-aware Unsupervised Multi-Document Summarization), which incorporates rich surrounding (topically related) documents to help improve the quality of extractive summarization without human supervision. Speci fically, we propose a joint optimization algorithm to unify global novelty (i.e., category-level frequent and discriminative), local consistency (i.e., locally frequent, co-occurring), and local saliency (i.e., salient from its surroundings) such that the obtained summary captures the characteristics of the target documents. Extensive experiments on news and scientifi c domains demonstrate the superior performance of our method when the unlabeled surrounding corpus is utilized.more » « less
-
Proceedings of the Sixteenth (Ed.)Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user’s interest. To model the semantic correlation between words and seeds for discovering topic-indicative terms, existing seedguided approaches utilize different types of context signals, such as document-level word co-occurrences, sliding window-based local contexts, and generic linguistic knowledge brought by pre-trained language models. In this work, we analyze and show empirically that each type of context information has its value and limitation in modeling word semantics under seed guidance, but combining three types of contexts (i.e., word embeddings learned from local contexts, pre-trained language model representations obtained from general-domain training, and topic-indicative sentences retrieved based on seed information) allows them to complement each other for discovering quality topics. We propose an iterative framework, SeedTopicMine, which jointly learns from the three types of contexts and gradually fuses their context signals via an ensemble ranking process. Under various sets of seeds and on multiple datasets, SeedTopicMine consistently yields more coherent and accurate topics than existing seed-guided topic discovery approaches.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2021.emnlp-main.11
