See-Liu-Manning Text Summarization Task

A See-Liu-Manning Text Summarization Task is an Abstractive Text Summarization Task that uses a pointer-generator model to produce summary from textual data.

• AKA: Text Summarization via Pointer-Generator Network.
• Context:
  • Task Input(s): Text Document (e.g.news articles)
  • Task Output(s): automatic generated summary.
  • Task Requirement(s):
    • Benchmark Datasets:
      • CNN + Daily Mail datasets from (Hermann et al., 2015; Nallapati et al., 2016).
    • Benchmark Performance Metrics:
      • ROUGE Metric (Lin, 2004);
      • Meteor Universal Metric (Denkowski & Lavie, 2014)
    • Baseline Models:
      • Sequence-to-Sequence Attentional Model (seq-to-seq + attn);
      • Pointer-Generator Model with and without Coverage Mechanism (pointer-generator, pointer-generator+covarage).
  • It can be solved by See-Liu-Manning Text Summarization System that implements See-Liu Manning Text Summarization Algorithms.
• Example(s):
  • See et al. (2017) input output examples:

     

     .

  • See et al. (2017) Abstractive+ Extractive models Performance metrics:

	ROUGE			METEOR
	1	2	L	exact match	+ stem/syn/para
abstractive model (Nallapati et al., 2016)*	35.46	13.30	32.65	-	-
seq-to-seq + attn baseline (150k vocab)	30.49	11.17	28.08	11.65	12.86
seq-to-seq + attn baseline (50k vocab)	31.33	11.81	28.83	12.03	13.20
pointer-generator	36.44	15.66	33.42	15.35	16.65
pointer-generator + coverage	39.53	17.28	36.38	17.32	18.72
lead-3 baseline (ours)	40.34	17.70	36.57	20.48	22.21
lead-3 baseline (Nallapati et al., 2017)*	39.2	15.7	35.5	-	-
extractive model (Nallapati et al., 2017)*	39.6	16.2	35.3	-	-

• Counter-Example(s):
  • CJS Neural Narrative Text Generation Task,
  • GSGAN Benchmark Task,
  • LeakGAN Benchmark Task.
  • MaliGAN Benchmark Task,
  • MaskGAN Benchmark Task,
  • RankGAN Benchmark Task,
  • SeqGAN Benchmark Task,
  • TextGAN Benchmark Task,
  • Texygen Benchmark Task.
• See: Natural Language Generation Task, Text Generation Task, Text Summarization Task, Sequence-to-Sequence Model, Neural Machine Translation, Encoder-Decoder Neural Network, Artificial Neural Network, Natural Language Processing Task, Language Model, Coverage Model.

---

---

References

2017

• (See et al., 2017) ⇒ Abigail See, Peter J. Liu, and Christopher D. Manning. (2017). “Get To The Point: Summarization with Pointer-Generator Networks.” In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). DOI:10.18653/v1/P17-1099.

2016

• (Nallapati et al., 2016) ⇒ Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos Santos, Caglar Gulcehre, and Bing Xiang. (2016). “Abstractive Text Summarization Using Sequence-to-sequence RNNs and Beyond.” In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL 2016). DOI:10.18653/v1/K16-1028.

2015

• (Hermann et al., 2015) ⇒ Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. (2015). “Teaching Machines to Read and Comprehend.” In: Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS'15). arXiv:1506.03340v3.

2014

• (Denkowski & Lavie, 2014) ⇒ Michael J. Denkowski, and Alon Lavie. (2014). “Meteor Universal: Language Specific Translation Evaluation for Any Target Language". In: Proceedings of the Ninth Workshop on Statistical Machine Translation (WMT@ACL 2014). DOI:10.3115/v1/W14-3348.

2004

• (Lin, 2004) ⇒ Chin-Yew Lin. (2004)."Looking for a Few Good Metrics: Automatic Summarization Evaluation - How Many Samples Are Enough?". In: Proceedings of the Fourth NTCIR Workshop on Research in Information Access Technologies Information Retrieval, Question Answering and Summarization (NTCIR 2004).

---