Topic-Aware Neural Keyphrase Generation for Social Media Language

Wang, Yue; Li, Jing; Chan, Ho-Leung; King, Irwin; Lyu, Michael R.; Shi, Shuming

doi:10.18653/v1/p19-1240

Cited by 69 publications

(60 citation statements)

References 28 publications

(42 reference statements)

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“…They report improvements on keyphrase diversity measured using distinct-1 and distinct-2 metrics described in [77]. In [78] they create another hybrid system that infuses topical information into the encoder-decoder framework. They use an NTM (Neural Topic Model) for grasping the latent topic aspects of the input text.…”

Section: B Enhanced and Hybrid Solutionsmentioning

confidence: 99%

Keyphrase Generation: A Multi-Aspect Survey

Çano

Bojar

2019

2019 25th Conference of Open Innovations Association (FRUCT)

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Extractive keyphrase generation research has been around since the nineties, but the more advanced abstractive approach based on the encoder-decoder framework and sequenceto-sequence learning has been explored only recently. In fact, more than a dozen of abstractive methods have been proposed in the last three years, producing meaningful keyphrases and achieving state-of-the-art scores. In this survey, we examine various aspects of the extractive keyphrase generation methods and focus mostly on the more recent abstractive methods that are based on neural networks. We pay particular attention to the mechanisms that have driven the perfection of the later. A huge collection of scientific article metadata and the corresponding keyphrases is created and released for the research community. We also present various keyphrase generation and text summarization research patterns and trends of the last two decades.

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Section: B Enhanced and Hybrid Solutionsmentioning

confidence: 99%

Keyphrase Generation: A Multi-Aspect Survey

Çano

Bojar

2019

2019 25th Conference of Open Innovations Association (FRUCT)

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“…Moreover, to well align the images' semantics to texts', we adopt image wordings and define two forms for that -explicit optical characters (such as "NBA Finals" in post (b)) detected from the optical character reader (OCR) and implicit image attributes (Wu et al, 2006), high-level text labels predicted to summarize the image's semantic concepts (such as a "cat" label for post (a)). Furthermore, unlike prior work employing either classification or generation models (Wang et al, 2019a), we propose a unified framework to couple the advantages of keyphrase classification and generation. Specifically, in addition to the joint training of both modules, we further extend the copy mechanism (See et al, 2017) to explicitly aggregate classification outputs together with tokens from the source input.…”

Section: Introductionmentioning

confidence: 99%

Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings

Wang

Lyu

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Social media produces large amounts of contents every day. To help users quickly capture what they need, keyphrase prediction is receiving a growing attention. Nevertheless, most prior efforts focus on text modeling, largely ignoring the rich features embedded in the matching images. In this work, we explore the joint effects of texts and images in predicting the keyphrases for a multimedia post. To better align social media style texts and images, we propose: (1) a novel Multi-Modality Multi-Head Attention (M 3 H-Att) to capture the intricate cross-media interactions; (2) image wordings, in forms of optical characters and image attributes, to bridge the two modalities. Moreover, we design a novel unified framework to leverage the outputs of keyphrase classification and generation and couple their advantages. Extensive experiments on a large-scale dataset 1 newly collected from Twitter show that our model significantly outperforms the previous state of the art based on traditional co-attentions. Further analyses show that our multi-head attention is able to attend information from various aspects and boost classification or generation in diverse scenarios.

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“…In this work, we define two kinds of agents for two generation tasks which can interact and share useful information. We also notice that in other areas, there are also some works (Xing et al, 2017;Wang et al, 2019c) consider incorporating topic information.…”

Section: Case Studymentioning

confidence: 65%

Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication

Wang

Wei

Cheng

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we propose a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism. We validate our approach on VIST dataset, where quantitative results, ablations, and human evaluation demonstrate our method's good ability in generating stories with higher quality compared to state-of-the-art methods.

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Topic-Aware Neural Keyphrase Generation for Social Media Language

Cited by 69 publications

References 28 publications

Keyphrase Generation: A Multi-Aspect Survey

Keyphrase Generation: A Multi-Aspect Survey

Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings

Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication

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