Personality-dependent Neural Text Summarization

In Natural Language Generation systems, personalization strategies - i.e, the use of information about a target author to generate text that (more) closely resembles human-produced language - have long been applied to improve results. The present work addresses one such strategy - namely, the use of Big Five personality information about the target author - applied to the case of abstractive text summarization using neural sequence-to-sequence models. Initial results suggest that having access to personality information does lead to more accurate (or human-like) text summaries, and paves the way for more robust systems of this kind.

[1]  Eduard H. Hovy,et al.  Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics , 2003, NAACL.

[2]  Houfeng Wang,et al.  Learning Summary Prior Representation for Extractive Summarization , 2015, ACL.

[3]  Nathan Hartmann,et al.  Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks , 2017, STIL.

[4]  Jason Weston,et al.  A Neural Attention Model for Abstractive Sentence Summarization , 2015, EMNLP.

[5]  Christopher D. Manning,et al.  Get To The Point: Summarization with Pointer-Generator Networks , 2017, ACL.

[6]  Ivandré Paraboni,et al.  Building a Corpus for Personality-dependent Natural Language Understanding and Generation , 2018, LREC.

[7]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[8]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[9]  Lucia Specia,et al.  BLEU Deconstructed: Designing a Better MT Evaluation Metric , 2013, Int. J. Comput. Linguistics Appl..

[10]  K. Scherer,et al.  The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance , 2011, Behavior research methods.

[11]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[12]  Marilyn A. Walker,et al.  PERSONAGE: Personality Generation for Dialogue , 2007, ACL.

[13]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[14]  A. Tellegen,et al.  PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES An Alternative "Description of Personality": The Big-Five Factor Structure , 2022 .

[15]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[16]  Geoffrey Zweig,et al.  Linguistic Regularities in Continuous Space Word Representations , 2013, NAACL.

[17]  Yejin Choi,et al.  Deep Communicating Agents for Abstractive Summarization , 2018, NAACL.

[18]  Xiaojun Wan,et al.  Improved Affinity Graph Based Multi-Document Summarization , 2006, NAACL.

[19]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[20]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[21]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[22]  M. de Rijke,et al.  Leveraging Contextual Sentence Relations for Extractive Summarization Using a Neural Attention Model , 2017, SIGIR.

[23]  Anja Belz,et al.  An Investigation into the Validity of Some Metrics for Automatically Evaluating Natural Language Generation Systems , 2009, CL.

[24]  Bowen Zhou,et al.  Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond , 2016, CoNLL.

[25]  Hwee Tou Ng,et al.  Better Evaluation Metrics Lead to Better Machine Translation , 2011, EMNLP.

[26]  Hermann Ney,et al.  LSTM Neural Networks for Language Modeling , 2012, INTERSPEECH.

[27]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..