Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
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1 Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised Stefanos Angelidis and Mirella Lapata Institute for Language, Cognition, and Computation School of Informatics, University of Edinburgh
2 Online User Reviews Heavily influence customer decisions: Travel booking (Ye et al., 2009) Box Office success (Duan et al., 2008) Shopping (TurnTo.com report, 2018) Incredibly rich data source: Yelp reviews: 6.3 million in million in 2017 Amazon reviews: 143 million from 1996 to /20
3 Extractive Opinion Summarization 2/20
4 Extractive Opinion Summarization 2/20
5 Extractive Opinion Summarization 2/20
6 Extractive Opinion Summarization Approach: Neural-based, Weakly Supervised 2/20
7 This Work Task decomposition Aspect Detection Fine-grained Sentiment Prediction (Angelidis and Lapata, TACL 2018) Summary Construction No direct supervision for aspects or sentiment No gold-standard summaries for training Newly-constructed OpoSum corpus 3/20
8 Aspect Detection Predict the aspect discussed in each review segment Television Review Overall a good TV! Such great picture quality. The colors are perfectly crisp. The sound is the only issue. If you like good bass, you must connect it to a Hi-Fi. A good value tv too. General Image Image Sound Sound Sound+Connect. Price Got enough annotated data? Just do classification! What if we don t? 4/20
9 Aspect Discovery vs. Extraction Discovery No prior knowledge about aspects Use topic modeling Post-hoc mapping from topics to aspects Neural method: Aspect-Based Autoencoder (ABAE; He et al., 2017) Extraction Inject prior knowledge via seed words No ability to discover new aspects Model guided towards aspects of interest Neural method: Multi-Seed Aspect Extractor (MATE; this work) 5/20
10 Previous Work: Aspect-Based Autoencoder (ABAE) Segment encoding: v = i c ix i Aspect dictionary: Aspect Matrix A = [a 1,..., a k ] Aspect prediction: p = softmax(w c v i + b c ) Segment reconstruction: r = A T p Objective: Reconstruction Max-Margin 6/20
11 Previous Work: Aspect-Based Autoencoder (ABAE) Segment encoding: v = i c ix i Aspect dictionary: Aspect Matrix A = [a 1,..., a k ] Aspect prediction: p = softmax(w c v i + b c ) Segment reconstruction: r = A T p Objective: Reconstruction Max-Margin 6/20
12 Multi-seed Aspect Extractor (MATE) Segment encoding: v = i c ix i Aspect dictionary: Seed Matrices {A 1,... A k } Aspect Vector a i = A T i z i Aspect Matrix A = [a 1,..., a k ] Aspect prediction: p = softmax(w c v + b c ) Segment reconstruction: r = A T p Objective: Reconstruction Max-Margin 7/20
13 Multi-seed Aspect Extractor (MATE) Segment encoding: v = i c ix i Aspect dictionary: Seed Matrices {A 1,... A k } Aspect Vector a i = A T i z i Aspect Matrix A = [a 1,..., a k ] Aspect prediction: p = softmax(w c v + b c ) Segment reconstruction: r = A T p Objective: Reconstruction Max-Margin 7/20
14 Multi-seed Aspect Extractor (MATE) Segment encoding: v = i c ix i Aspect dictionary: Seed Matrices {A 1,... A k } Aspect Vector a i = A T i z i Aspect Matrix A = [a 1,..., a k ] Aspect prediction: p = softmax(w c v + b c ) Segment reconstruction: r = A T p Objective: Reconstruction Max-Margin 7/20
15 Multi-seed Aspect Extractor (MATE) Segment encoding: v = i c ix i Aspect dictionary: Seed Matrices {A 1,... A k } Aspect Vector a i = A T i z i Aspect Matrix A = [a 1,..., a k ] Aspect prediction: p = softmax(w c v + b c ) Segment reconstruction: r = A T p Objective: Reconstruction Max-Margin 7/20
16 Multi-task Learning (MATE+MT) Goal: Help encoder focus on aspect words Idea: Aspect words are domain-specific! The colors are perfectly crisp The keys feel great to type on Domain prediction: p dom = softmax(w d v + b d ) Combined objective: Reconstruction Max-Margin NLL of domain prediction 8/20
17 Extractive Opinion Summarization 9/20
18 Extractive Opinion Summarization 9/20
19 Fine-grained Sentiment Prediction Multiple Instance Learning Network (MilNet) Training examples bags of instances Bag labels supervision Instance labels latent 10/20
20 Fine-grained Sentiment Prediction Multiple Instance Learning Network (MilNet) Training examples bags of instances Bag labels supervision Instance labels latent Model output: pol s [ 1, +1] 10/20
21 Extractive Opinion Summarization 11/20
22 Extractive Opinion Summarization 11/20
23 Opinion Salience Two sources of information: Segment aspect probabilities (via MATE) Segment polarities (via MilNet) No further training required What makes a useful opinion? Needs to be non-neutral: It has 2 speakers You can barely hear it Needs to be non-general: Loved it! Many input ports to choose from 12/20
24 Opinion Salience Two sources of information: Segment aspect probabilities (via MATE) Segment polarities (via MilNet) No further training required What makes a useful opinion? pol s (max i p (a i) s Needs to be non-neutral: It has 2 speakers You can barely hear it Needs to be non-general: Loved it! Many input ports to choose from p (GEN) s ) 12/20
25 From Rankings to Summaries Need to avoid redundancy Greedy opinion selection: (Cao et al., 2015; Yasunaga et al., 2017) Start by adding top-ranked opinion to summary Keep adding unless: a. Cosine similarity with any previous opinion is over 0.5 b. Summary budget reached (100 words) 13/20
26 The OpoSum Corpus Training Data What we need Diverse domains Thousands of reviews No fine-grained labels Training Data Source Amazon Domain Lapt. Bags Bluetooth Boots Keyboards Televisions Vacuums Products Reviews 43K 80K 78K 34K 57K 68K Words / Review /20
27 The OpoSum Corpus Evaluation Data What we need Aspect labels Opinion salience labels Final summaries 15/20
28 The OpoSum Corpus Evaluation Data What we need Aspect labels Opinion salience labels Final summaries Television Review Overall a good TV! Such great picture quality. The colors are perfectly crisp. The sound is the only issue. If you like good bass, you must connect it to a Hi-Fi. A good value tv too. General Image Image Sound Sound Sound+Connect. Price 15/20
29 The OpoSum Corpus Evaluation Data What we need Aspect labels Opinion salience labels Final summaries Television Review Overall a good TV! Such great picture quality. The colors are perfectly crisp. The sound is the only issue. If you like good bass, you must connect it to a Hi-Fi. A good value tv too. 15/20
30 The OpoSum Corpus Evaluation Data What we need Aspect labels Opinion salience labels Final summaries Television Review 1 What a joke! Terrible, terrible sound! I ve tried everything to fix it, but no luck. Television If you like good Review bass, 2 I you had must no high connect expectations, it to a Hi-Fi. so A good I was value pleasantly tv too. surprised. The image on this is great. So are the applications. If you Television like good Review bass, 3 you Overall must connect a good TV! it to a Hi-Fi. A good Such value great picture tv too. quality. The colors are perfectly crisp. The sound is the only issue. If you like good bass, you must connect it to a Hi-Fi. A good value tv too. Television Opinion Summary The image on this is great. The colors are perfectly crisp. A good value tv too. Terrible, terrible sound! 15/20
31 The OpoSum Corpus Evaluation Data What we need Aspect labels Opinion salience labels Final summaries Evaluation Data Domain Lapt. Bags Bluetooth Boots Keyboards Televisions Vacuums Products Reviews Segments /20
32 Results Aspect Extraction Micro-F ABAE ABAE init MATE MATE+MT 30 L.Bags Bluetooth Boots Keyb/s TV Vac/s 16/20
33 Results Salient Opinion Retrieval MAP MilNet MATE MATE+MT MilNet+MATE+MT L.Bags Bluetooth Boots Keyb/s TV Vac/s 17/20
34 Results Opinion Summarization ROUGE-L Lead SumBasic LexRank Opinosis MilNet+MATE+MT 30 L.Bags Bluetooth Boots Keyb/s TV Vac/s 18/20
35 Results Human Evaluation Aggregate Score Best-Worst Scaling (Louviere et al. 2015) Gold Lead Opinosis This work Informativeness Polarity Coherence Redundancy 19/20
36 Summary Opinion summarization without direct supervision Combines weakly supervised sentiment and aspect detectors Multi-tasking improves aspect extraction Automatic and human evaluation show promising results 20/20
37 ...and some of our summaries: Boots The attention to detail is great! I love the colour plum. Sooooooooo narrow! They have stretched a little, but maybe get a size or half size up. They do not fit well, horribly uncomfortable. TV The sound is good and strong. The picture is beautiful. And the price is even better. Unbelieveable picture and the setup is so easy. The Yahoo widgets don t work. And avoid the Sony apps at ALL costs. Communication a bit difficult. :( Thank you! Data and Code: stangelid.github.io
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